Gateway alternatives · 2026-05-21
OpenRouter alternative for Turkey: how to choose an OpenAI-compatible LLM API
A practical comparison framework for teams evaluating a multi-model gateway with a Turkey-focused control layer.
LLMTR Blog
Guides on Turkey-focused LLM APIs, OpenAI-compatible gateways, model comparison, pricing, and data policy.
Gateway alternatives · 2026-05-21
A practical comparison framework for teams evaluating a multi-model gateway with a Turkey-focused control layer.
LLM gateway basics · 2026-05-21
A concise explanation of the LLM gateway layer and why production teams use it for multi-model applications.
Integration guides · 2026-05-21
The minimum changes needed to point an existing OpenAI SDK client at the LLMTR gateway.
Model comparison · 2026-05-21
A practical decision matrix for comparing provider models through one API surface.
Trust and compliance · 2026-05-21
A practical framing for data-processing decisions when using LLM APIs in Turkey-focused products.
Pricing and benchmark · 2026-05-21
A clear way to calculate LLM API cost using token units, catalog model prices, and the credit top-up margin.
LLM gateway basics · 2026-05-22
A practical starting point for teams planning AI API integrations across endpoints, models, security, pricing, and data-processing decisions.
Gateway alternatives · 2026-05-22
A practical framework for evaluating OpenAI API alternatives by compatibility, model coverage, data policy, cost, and migration effort.
Integration guides · 2026-05-22
A practical guide to calling Gemini models from OpenAI-compatible clients while separating endpoint compatibility from model capability.
Agent and MCP guides · 2026-05-22
A technical guide for evaluating Claude models in coding-agent and long-context workflows across quality, cost, security, and gateway choices.
Agent and MCP guides · 2026-05-22
A practical explanation of Model Context Protocol in agent applications, including data access, tools, permissions, and gateway decisions.
Agent and MCP guides · 2026-05-22
A production architecture view of model calls, tool calls, security boundaries, usage metering, and cost controls for AI agent systems.
RAG and data guides · 2026-05-22
A technical overview of RAG architecture across company data, embeddings, retrieval, data security, model choice, and gateway cost control.
Trust and compliance · 2026-05-22
A security checklist for LLM API integrations covering API keys, rate limits, prompt data policy, and cost safety.
Pricing and benchmark · 2026-05-22
A practical cost model for LLMs across catalog price, real token usage, context, reasoning, free-model risk, and credit top-up margin.
Agent and MCP guides · 2026-05-22
A practical framework for choosing coding-agent models by context, patch quality, test loops, tool use, security, and gateway cost.
Agent and MCP guides · 2026-05-23
A practical explanation of Antigravity after Google I/O 2026 for teams evaluating Gemini API agents and multi-model gateways.
Integration guides · 2026-05-23
A migration framework for Gemini CLI users evaluating Antigravity CLI, focused on settings, MCP, security, and model cost.
Agent and MCP guides · 2026-05-23
A technical explanation of Managed Agents and how they differ from classic chat endpoints in production gateway design.
Model comparison · 2026-05-23
A practical model-selection guide for Gemini 3.5 Flash in agent and coding workflows, focused on quality, speed, and cost.
Integration guides · 2026-08-28
Gemini Omni Flash now offers video generation and editing through Google's API. This guide separates access, media permissions, and application rollout checks.
Agent and MCP guides · 2026-08-28
Spark is a personal agent in Gemini Apps. Tasks and schedules are distinct from connected-app access, automatic actions, and the user's responsibility to supervise.
Pricing and benchmark · 2026-05-23
A practical guide to Gemini API cost across token usage, model choice, free-tier expectations, API key safety, and LLMTR credits.
Agent and MCP guides · 2026-05-26
A production-oriented guide to the Gemini Interactions API, focused on state, background work, webhooks, usage metering, and gateway boundaries.
LLM gateway basics · 2026-05-23
A practical explanation of the LLMTR AI Gateway layer across one API, model catalog, API keys, usage metering, and Turkey-focused AI infrastructure.
LLM gateway basics · 2026-05-23
A clear distinction between AI gateway and LLM gateway terminology, where LLMTR fits, and how teams should choose based on production needs.
Integration guides · 2026-05-23
A practical guide to the minimum settings needed when moving an existing OpenAI SDK application to the LLMTR AI Gateway.
Trust and compliance · 2026-05-25
A practical response guide for Google and Gemini API key leaks, covering early containment, budget alerts versus hard limits, and proxy-based cost controls.
Integration guides · 2026-05-27
A production-focused guide for moving Google AI Studio Android prototypes into a secure Gemini API and LLMTR gateway architecture.
Pricing benchmarks · 2026-05-28
A practical guide to controlling tokens, retries, batch work, usage tracking, and budget limits before sending agentic coding traffic to production.
Trust and compliance · 2026-05-28
A production-focused guide for sandbox boundaries, tool permissions, egress, approvals, and provider-key safety in long-running agent workflows.
RAG and data · 2026-05-28
How to add X/Twitter social signals to an LLM agent or RAG flow while controlling tool invocation cost, data boundaries, model choice, and the LLMTR gateway.
Agent workflows · 2026-05-31
Connect Grok Build 0.1 API interest to coding-agent architecture, model choice, MCP-enabled workflows, and LLMTR gateway cost controls.
Integration guides · 2026-05-31
Read OpenAI model changes with a clear ChatGPT-versus-API distinction, then turn them into safer fallback, testing, and cost controls in the LLMTR gateway.
RAG and data · 2026-05-31
Explains how technical products like LLMTR can build source-worthy, measurable, and reliable blog and documentation surfaces for AI Mode and AI Overviews.
RAG and data · 2026-06-02
A practical guide to measuring AI search visibility for technical products through citations, referrals, source quality, freshness, and structured content surfaces.
RAG and data · 2026-06-02
A practical guide to measuring AI chatbot visits through citations, referrals, conversions, and technical log validation instead of vanity metrics.
Trust and compliance · 2026-06-02
A practical guide to AI crawler access that separates training from search and treats llms.txt as a controlled content map, not a ranking guarantee.
RAG and data · 2026-06-03
Connects Google's new AI Search control and measurement signals to the LLMTR publishing standard across blog, docs, llms.txt, and usage tracking.
RAG and data · 2026-06-03
Explains how to measure AI Overviews and AI Mode impact with Search Console, GA4, server logs, and product funnel data instead of one vanity metric.
Trust and compliance · 2026-06-03
Explains how to become a preferred and citable source in AI Search through original technical content, clear docs, trust boundaries, and measurement discipline.
AI search and data · 2026-06-04
Turns Gemini-assisted Google Trends exploration into a measured AI Search/GEO keyword workflow for LLMTR content planning.
AI search and data · 2026-06-04
Explains how to structure LLMTR content for longer, decision-heavy AI Mode queries with technical accuracy and measurement.
Model comparisons · 2026-06-04
A publishing-calendar approach that connects new AI model announcements to model choice, gateway migration, cost, and AI Search source strategy.
AI search and data · 2026-06-04
Explains Bing Webmaster Tools AI Performance for LLMTR citation visibility, source measurement, content improvement, and funnel analysis.
Trust and compliance · 2026-06-04
Explains how to separate OAI-SearchBot, GPTBot, noindex, referral UTM, and LLMTR security boundaries when targeting ChatGPT Search visibility.
AI search and data · 2026-06-06
A practical guide for using Google Trends RSS and export surfaces to choose, filter, and measure current AI/API keywords for LLMTR.
Pricing and comparison · 2026-06-06
Separates OpenAI model updates into ChatGPT and API impact, then gives a code, cost, and sunset checklist for GPT-5.5 Instant update work.
Pricing and comparison · 2026-06-06
Explains quality, latency, token efficiency, fast-mode tradeoffs, and LLMTR gateway cost control for Claude Opus 4.8 API agent workflows.
Model comparison · 2026-06-06
Connects xAI Grok Imagine 1.5 API preview signals to image-to-video model selection, safe payloads, quality measurement, and LLMTR multimodal gateway strategy.
Agent workflows · 2026-06-06
Connects xAI Grok Voice and Vapi signals to voice AI agent architecture, STT/TTS separation, latency budget, safety, and LLMTR usage measurement.
RAG and data · 2026-06-11
Turns World Cup 2026 attention into a source-backed trend monitoring and LLM API summarization workflow.
RAG and data · 2026-06-11
Designs transfer-trend summarization as source verification, deduplication, and LLM API cost control instead of rewritten rumors.
Trust and compliance · 2026-06-11
Treats visa and Schengen attention as a sensitive, source-backed, privacy-aware AI summary workflow.
Agent workflows · 2026-06-11
Frames vehicle inspection and fee questions as a real operations automation workflow with document checks, call-center routing, and safe LLM agents.
Agent workflows · 2026-06-11
Turns high-intent public hiring result searches into an official-source monitoring and controlled AI notification workflow.
LLM gateway basics · 2026-06-12
Positions LLMTR as the gateway layer for teams evaluating Turkish and global LLM models through one API, catalog, pricing, and data-policy surface.
Model comparison · 2026-06-12
Turns the local AI model search into API, data-policy, pricing, Turkish-quality, and gateway-control criteria for production systems.
Model comparison · 2026-06-12
Connects Turkish LLM search intent to benchmarks, practical prompt tests, pricing, context windows, and model trials through one API.
Gateway alternatives · 2026-06-12
Connects Turkey LLM gateway search intent directly to LLMTR's OpenAI-compatible API, one API key, model catalog, usage tracking, and multi-provider architecture.
Agent workflows · 2026-06-12
Connects AI agent Turkey search intent to agent architecture, tool calling, MCP, Turkish model choice, safety, and LLMTR gateway usage.
Integration guides · 2026-06-14
Explains how to migrate to the GPT-5.5 API by reducing the change to a base URL and model identifier while keeping your existing OpenAI client.
Gateway alternatives · 2026-06-14
Explains how to migrate to a new model without breaking production as Claude Opus 4 and Sonnet 4 are retired.
Model comparison · 2026-06-14
Offers a practical LLM API comparison to pick models by task type instead of hunting for a single winner.
Agent workflows · 2026-06-14
Explains how to unify MCP server tool access with model calls on one OpenAI-compatible gateway surface.
RAG and data · 2026-06-14
Explains GEO generative engine optimization through citation-ready content structure and a tracking method built with an LLM API.
Integration guides · 2026-06-22
A preparation method for turning a student community idea into a measurable and feasible UNIDES application for the next official call.
Agent workflows · 2026-06-22
Project ideas and selection criteria that connect AI to real student needs across the 11 UNIDES policy areas without making technology the objective.
Integration guides · 2026-06-22
Connects application planning with technical delivery to turn a UNIDES idea into a secure, measurable, and budgeted AI prototype.
Trust and compliance · 2026-07-28
An introductory guide covering the strategy, institutional structure, regulation, and technical implementation layers of public sector AI work in Turkey, based on verifiable sources.
Model comparison · 2026-07-28
A review matching publicly announced AI applications in Turkish government institutions to the model classes those applications actually require.
Trust and compliance · 2026-07-28
An implementation guide covering the data security layers a public institution must satisfy when using language models: regulation, prompt hygiene, and technical controls.
LLM gateway basics · 2026-07-28
A technical guide to an architecture that solves data locality, provider independence, and cost control together in public sector language model integrations.
Agent and MCP guides · 2026-07-28
An implementation plan that moves a public sector AI pilot from idea to rollout, covering scope, measurement, human approval, and budget together.
Trust and compliance · 2026-07-28
A guide covering the professional secrecy, verification, and data security questions law firms and in-house legal teams face when adopting language models.
Trust and compliance · 2026-07-28
A guide covering the data locality, customer secrecy, and auditability questions bank, brokerage, and fintech teams face when adopting language models.
Trust and compliance · 2026-08-13
A practical guide to Article 9 of Law No. 6698 (KVKK) for Turkish public institutions: the three-tier cross-border transfer regime and the derogations that are closed to public bodies acting under public law.
Integration guides · 2026-08-13
Sets out the clauses an artificial intelligence software rental or service procurement specification needs, why each one matters, how it will be measured, and example texts that can be written straight into the document.
Trust and compliance · 2026-08-13
Quotes Article 3 of Presidential Circular No. 2019/12 in full, separates an outright ban from a conditional permission, and resolves whether LLM usage counts as cloud storage on a per-data-class basis.
Trust and compliance · 2026-08-13
A step-by-step view of how Turkish public institutions build the standard contract and notification chain for foreign language model providers, and how that load multiplies as providers are added.
Integration guides · 2026-08-13
A guide, grounded in the published regulatory texts, to which of Turkey's three public IT authorization certificates each type of work requires, what the ISO 27001 prerequisite means in practice, and what data responsibility a contractor inherits on AI projects.
Pricing and benchmark · 2026-08-13
Explains how Turkish public institutions can scope an AI pilot within the 2026 direct procurement thresholds of Law No. 4734, how to build a token-based budget, and why splitting a requirement is prohibited.
Trust and compliance · 2026-08-13
A public institution does not make one AI decision; it makes a separate architectural decision for every class of data it processes. This article applies the asset group and criticality rating logic of Turkey's Information and Communication Security Guide to large language model integrations and offers a workable mapping between data confidentiality level and model architecture.
Trust and compliance · 2026-08-13
A practical guide that maps the chapter headings of KVKK Publication No. 114 onto the steps of a public sector AI project, built on direct quotations from the source document.
Agent and MCP guides · 2026-08-13
An honest comparison of the three architecture options for public institutions building an in-house AI assistant, plus scoping, approved institutional source sets, human review, access separation and measurement.
Agent and MCP guides · 2026-08-13
In municipal call center and citizen solution-line projects, artificial intelligence is not a single piece of software but four distinct jobs with different model requirements. This article covers task decomposition, human handover thresholds, KVKK boundaries, how a lowest-price tender changes bid arithmetic, and the metrics worth writing into a technical specification.
Pricing and benchmark · 2026-08-13
The Türkiye Artificial Intelligence Action Plan 2026-2030 creates both an obligation and a funding channel for public agencies. This article covers the practical budget meaning of the 2 percent commitment, how to assemble an internal funding case, and why choosing a portable architecture is a risk management decision while regulation is still pending.
Model comparison · 2026-08-13
For public institutions evaluating domestic large language models: verified public data on BILGE, KUMRU and MAIN, task-to-model matching tables, and multi-model access through a single API.
Trust and compliance · 2026-08-18
A technical analysis translating the Cybersecurity Directorate's public-sector AI framework into concrete data, governance, and measurement outputs institutions can prepare today.
Integration guides · 2026-08-18
A source-based comparison of six needs published under TÜBİTAK call 1007-SGB-2026-01, identifying the recurring architecture patterns behind distinct government AI projects.
RAG and data · 2026-08-18
A technical analysis of the Turkish Constitutional Court's specification for AI-supported individual-application review, covering data, metrics, human oversight, and security boundaries.
RAG and data · 2026-08-18
An analysis of Turkey's YAZDIS textbook-review project that treats expert feedback, measurement, access control, and isolated operations as equally important as model capability.
Trust and compliance · 2026-08-18
A comparison of two public financial-risk projects across data control, explainable results, human assessment, security, and model lifecycle rather than model accuracy alone.
Model comparison · 2026-08-23
A technical guide for teams evaluating Tencent Hy3 for coding agents and long-document work: the gap between the advertised 256K and the real 192K limit, thinking modes, JSON schema support and prompt cache pricing.
Model comparison · 2026-08-23
A technical guide for teams evaluating DeepSeek's vision model: which image formats work, the 1M context and 384K output ceiling, what the peak-hour multiplier means in practice, and how to plan cost around it.
Model comparison · 2026-08-23
A technical guide covering the difference between Nemotron 3 Ultra's free and metered rows, the 262K context window, the block-based prompt cache mechanism and how reasoning behaves.
Model comparison · 2026-08-28
For anyone searching Ling 3.0 Tiny: what the model was, why the provider closed the route, why LLMTR does not silently reroute the request, and the two things that change when you migrate to Ling 3.0 Flash.
Model comparison · 2026-08-28
Gemini Spark is a consumer agent product. Compare conversation, scheduled content and application development to choose an approach that matches the work you want completed.
Model comparison · 2026-08-28
Total parameters describe the model's weights, while active parameters describe the portion used to process a token. Ling 3.0 Tiny illustrates what this distinction can and cannot tell you.
Pricing and benchmarks · 2026-08-28
Separate ordinary input, cached input and total output when estimating Contributor costs. Apply verified rates and track spending per successfully completed task.
Trust and compliance · 2026-08-28
Choosing Contributor is a decision about which data your application may send. Evaluate training use, the limits of standard-tier assurances, and controls before the request.
Integration guides · 2026-08-28
A practical guide to switching local Ling 3 Tiny reasoning on or off, limiting generation, and rejecting incomplete answers, with LLMTR availability explained separately.
RAG and data guides · 2026-08-28
Check Tiny's context, preserve evidence references and distinguish computation caching from API cache prices.
Integration guides · 2026-08-28
Ling-3.0-Tiny weights are available to download. Choose a package by checking its file size, runtime support and working memory requirements separately.
Integration guides · 2026-08-28
Serving Ling Tiny on your own hardware requires a matching runtime, weights directory, and client model name. This guide separates a local API from the retired LLMTR route.
Integration guides · 2026-08-28
Use the correct image content part and choose a verified path for PDF documents, separating transfer failures from incomplete or incorrect document reading.
RAG and data guides · 2026-08-28
Before sending a long document, divide the context budget into input, output, headroom and the total limit. Improve source selection using reported usage.
Integration guides · 2026-08-28
Calling Contributor through LLMTR requires the model ID, API base URL and credentials to match. This guide starts with the data warning, then builds a first text request and checks the relevant API contract.
Agent and MCP guides · 2026-08-28
Muse Spark Contributor proposes tool calls; your application decides whether to execute them. Validate arguments, restrict available operations and return each result with its matching call identifier.
Agent and MCP guides · 2026-08-28
Evaluate an agent workflow without copying customer records: use narrow tasks, reviewed examples, fixed expected values and local scoring.
Integration guides · 2026-08-28
Move from meta/muse-spark-1.2-contributor to meta/muse-spark-1.2 with a practical checklist for data approval, configuration, a trial request, and rollback.
Pricing and benchmarks · 2026-08-28
Budget Solar Pro 4 by pricing period, cache hits and total output tokens. A worked example compares promotional and standard rates without adding top-up margin.
Model comparison · 2026-08-28
Evaluate Solar Pro 4 on bounded text tasks, then design document preparation, Turkish acceptance tests and tool permissions as separate parts of your workflow.
Model comparison · 2026-08-28
Flash Fin offers a free alternative after Ling Tiny, but it is a separate finance-focused model, not an automatic successor. Paid Flash remains the replacement named in LLMTR's retirement policy.
Integration guides · 2026-08-28
Ling Tiny has a documented experimental path for Apple Silicon. Downloadable weights, code on the main branch and support in a stable application require different checks.
Integration guides · 2026-08-28
Learn the model identifier, required voice selection, and response format checks needed to turn Turkish text into a single voice recording through LLMTR.
Integration guides · 2026-08-28
Split narration at sentence boundaries, retain the same voice and delivery notes, validate audio formats and assess cost using actual text and audio token usage.
Integration guides · 2026-08-28
The Qwen-VL-OCR model identifier, provider region, and documentation address serve different purposes. Start with one image containing no personal data, then check the request format and deployment conditions.
RAG and data guides · 2026-08-28
A synthetic invoice workflow for image submission, unknown fields and independent total checks. JSON output is neither a payment instruction nor an accuracy guarantee.
Integration guides · 2026-08-28
A migration guide separating the official successor from the LLMTR identifier, with repeatable image checks and a provider example using a fixed version.
Model comparison · 2026-08-28
Pixtral 12B produces text from image and text inputs. Published weights, the provider's API support and LLMTR catalog status require separate checks.
Integration guides · 2026-08-28
Safe retries for Muse Spark Contributor require reading both HTTP status and error type. This guide excludes exhausted quotas from rapid retries and sets explicit waiting limits.
RAG and data guides · 2026-08-28
A response that parses as JSON is not necessarily a valid record or a faithful extraction. Keep these three checks separate when using Muse Spark Contributor.
Agent and MCP guides · 2026-08-28
Set up a bounded planning task for Gmail and Calendar, distinguishing a proposed action from a change to your account.
Agent and MCP guides · 2026-08-28
Check access, open Gemini Spark and review a research task with explicit boundaries.
Integration guides · 2026-08-28
Use the current Google model identifier, poll an Interaction correctly, edit a completed video with server-side state, and distinguish SDK output from REST steps.
Agent and MCP guides · 2026-08-28
Solar Pro 4 can propose a tool call; authorization, argument validation, local execution and result matching remain responsibilities of your application.
Model comparison · 2026-08-28
Solar Pro 4 offers a larger context and a separate output ceiling, while Pro 3 has a smaller workspace; choose by scoring the same prompt under two reasoning conditions.
Integration guides · 2026-08-29
Claude Code needs no modified build: base URL, credential and model identifier are supplied as environment variables. This guide covers model discovery, the context window warning and the unsupported fields separately.
Integration guides · 2026-08-29
Codex CLI now speaks only the Responses protocol, so the model you pick on LLMTR must carry a Responses binding as well. The provider table, model choice and error triage all follow from that one constraint.
Integration guides · 2026-08-29
OpenClaude defines LLMTR as its own gateway record with a dedicated base URL, a dedicated credential variable and a hybrid catalog fed by seed entries. This guide walks that record field by field and explains how the model list fills up.
Integration guides · 2026-08-29
In OpenCode the first-party llmtr routes arrive preloaded, while every other catalog identifier is added to the configuration file by hand. This guide covers the connect flow, the shape of the provider block and model naming.
Integration guides · 2026-08-29
OpenClaw does not bake the LLMTR provider into its core. It carries the integration as a package under the extensions directory with its own manifest and model catalog, and this guide unpacks both.
Integration guides · 2026-08-29
Cline picks a provider from a settings panel inside the editor and stores the chosen model separately for Plan and Act. This guide walks the panel flow, the request the handler builds and the SDK registration.
Agent workflows · 2026-08-29
Working from the actual files on the PentestCode LLMTR branch, this guide covers key resolution, the seeded and live model catalog, and the request transform that normalises reasoning fields per model, all inside an authorised engagement scope.
Agent workflows · 2026-08-29
Built from the real files on two separate Strix branches, this guide walks through model prefix resolution, why the dedupe model needs its own endpoint, and which Turkey-hosted rows actually support the tool calls an agent loop needs.
Agent workflows · 2026-08-29
Connecting the LLMTR provider in HarnessRouter adds no new name to any model picker; it makes models already listed reachable through Türkiye. Here is the intersection logic and the wire shape each harness uses.
Integration guides · 2026-08-29
Hermes Agent keeps third-party providers out of the core tree and in a plugin directory. The LLMTR profile is a declarative ProviderProfile with no custom transport hooks; this guide covers its fields and its lifecycle.
Integration guides · 2026-08-29
In DeepSeek Harness a provider integration is not written into the core; it arrives as a plugin attached to the ctx.llm seam. The model list is not bundled either, it is read from the endpoint.
Integration guides · 2026-08-29
Pi registers a provider through a small factory function and leaves the model list to a generation script rather than maintaining it by hand. This guide explains how those two files relate and what the request body contains.
Integration guides · 2026-08-29
Trae Agent reaches LLMTR through a dispatching class that splits models across two client implementations. This guide walks the Python configuration, the routing decision and the fields visible in the run record.
Integration guides · 2026-08-29
Configuration on the pool side is entirely environment variables. This guide covers the three of them and shows why the Laguna list in the README has drifted from today's catalogue.
Agent workflows · 2026-08-29
In a workflow engine a model node builds its client from the selected credential and then resolves the model. Because LLMTR is a gateway, that resolution reads two separate sources: wire shape from the vendor, thinking from the gateway.
Integration guides · 2026-08-29
OpenStory is a multi-team production platform, so the key is not a single environment variable. An LLMTR key can be entered per team, and it outranks the other providers whenever the gateway carries the requested model.
Integration guides · 2026-08-29
OpenReview explores the repository on every review, so token spend follows the model you pick. In this branch swapping the gateway is not a code change but a matter of three environment variables.
Integration guides · 2026-08-29
OpenBot is a self-hosted system that can give every agent a computer of its own. The integration is a matter of environment values rather than code, and a single base URL redirects three separate readers at once.
Integration guides · 2026-08-29
Open SEO is an open source SEO analysis platform whose agent features are optional. This guide adds a second path without removing the default one, and shows the precedence rule and where spend visibility moves to.
Integration guides · 2026-08-29
Open WebUI treats a gateway catalog as if every entry were a chat model. This guide sets up the connection, inspects the catalog without a key, and narrows the picker to models that can actually hold a conversation.
Model comparison · 2026-08-29
models.dev is not a service but an open metadata database holding model ids and capabilities in TOML files. We compare the current LLMTR entry against the live catalog to see which rows are missing and which one is retired.
Integration guides · 2026-08-29
Classifies the 21 developer tools that support LLMTR into three integration shapes and shows where configuration happens for each one.
GPT-6 Astra guides · 2026-09-05
Explore GPT-6 Astra, OpenAI’s new model for complex work: its context window, reasoning, access rollout and practical considerations for API developers.
GPT-6 Astra guides · 2026-09-05
Calculate GPT-6 Astra API costs with standard and long-context rates, cache write and read charges, and a worked example that separates input from output.
GPT-6 Astra guides · 2026-09-05
Send your first GPT-6 Astra request through LLMTR with Responses API, choose a supported reasoning level and inspect output usage before scaling up.