MiniMax M3.1 Flash vs Haiku 5.5, GPT-6 Luna, Gemini 3.8 Flash

Short answer: Haiku and Luna cost less on short prompts, MiniMax on long ones

LLMTR is a gateway that gives access to language models from different providers through one OpenAI-compatible API. MiniMax M3.1 Flash (minimax/minimax-m3.1-flash), Claude Haiku 5.5 (anthropic/claude-haiku-5.5), GPT-6 Luna (openai/gpt-6-luna) and Gemini 3.8 Flash (google/gemini-3.8-flash) are called here on the /v1/chat/completions endpoint at https://llmtr.com/v1 with the same API key from your llmtr.com account. All four have a context window of about 1 million tokens, and moving from one to another is, for most requests, a change of the model string.

The length of the prompt decides the price order. For prompts up to 100,000 tokens, Claude Haiku 5.5 and GPT-6 Luna, at $0.10 input and $0.50 output per 1M tokens, are below MiniMax M3.1 Flash's $0.15 and $0.60. Haiku 5.5 bills the whole request at a higher price once the prompt passes 100,000 tokens, and GPT-6 Luna once it passes 272,000 tokens. MiniMax M3.1 Flash's price does not change up to 1,000,000 tokens; for prompts over 272,000 tokens it costs less than the other three for the same token count. What makes you pick Gemini 3.8 Flash is not price but input types: it is the only one of the four that accepts audio.

• MiniMax, Pay-as-you-go pricing · 2026-10-19
• Anthropic, Claude Haiku 5.5 overview · 2026-10-19
• OpenAI, GPT-6 Luna model page · 2026-10-19
• Google, Gemini API pricing · 2026-10-19

MiniMax M3.1 Flash, Claude Haiku 5.5, GPT-6 Luna or Gemini 3.8 Flash?

Three questions decide it: how long your prompts are, which input types you send, and how much output you need in one response. In chat, classification or summarization traffic where most prompts stay under 100,000 tokens, Claude Haiku 5.5 and GPT-6 Luna lead on price. For prompts that carry a long document, a large codebase or a long conversation history, MiniMax M3.1 Flash's single-tier price moves ahead. If you send video, the options are MiniMax M3.1 Flash and Gemini 3.8 Flash.

For work that needs a long output in one response the limits diverge: MiniMax M3.1 Flash 524,288, Claude Haiku 5.5 and GPT-6 Luna 128,000, and Gemini 3.8 Flash 65,536 tokens. If you need JSON that follows a schema, GPT-6 Luna and Gemini 3.8 Flash list structured outputs on their cards; MiniMax M3.1 Flash supports JSON object mode and does not hold to a strict schema. The table shows the four rows with the values in the LLMTR catalog; prices are per 1M tokens, in the order input / cached input / output.

• Google, Gemini 3.8 Flash model page · 2026-10-19

What does the same request cost on each model?

Without a cache hit, and assuming the same token counts on all four models, a 20,000-token prompt with a 2,000-token answer costs $0.0042 on MiniMax M3.1 Flash, $0.003 on Claude Haiku 5.5 and GPT-6 Luna, and $0.0225 on Gemini 3.8 Flash. With a 150,000-token prompt Haiku 5.5 moves to its higher tier: with the same answer GPT-6 Luna costs $0.016, MiniMax M3.1 Flash $0.0237, Haiku 5.5 $0.08 and Gemini 3.8 Flash $0.12. With a 300,000-token prompt GPT-6 Luna moves to its higher tier too: MiniMax M3.1 Flash $0.0462, GPT-6 Luna $0.0615, Haiku 5.5 $0.155 and Gemini 3.8 Flash $0.2325.

Read this calculation with one limit in mind: the four models use different tokenizers, the same text maps to a different number of tokens on each, and reasoning tokens are billed at the output price. To see the real cost, send the same prompt to all four. The Python example below sends a report to the four models, reads the token counts in usage, and works out the cost at the higher tier's price when the prompt has passed the threshold; it leaves the cache discount out. reasoning_effort: low is accepted by all four models.

OpenAI Python SDK example that sends the same report to the four models through https://llmtr.com/v1/chat/completions and prints token usage and the cost by prompt threshold.

import os

from openai import OpenAI

client = OpenAI(base_url="https://llmtr.com/v1", api_key=os.environ["LLMTR_API_KEY"])

# Per 1M tokens: (input, output), the prompt threshold, (input, output) above it.
# A prompt over the threshold bills the whole request at the second pair.
# Gemini 3.8 Flash: introductory price through 31 December 2026.
PRICES = {
    "minimax/minimax-m3.1-flash": ((0.15, 0.60), None, None),
    "anthropic/claude-haiku-5.5": ((0.10, 0.50), 100_000, (0.50, 2.50)),
    "openai/gpt-6-luna": ((0.10, 0.50), 272_000, (0.20, 0.75)),
    "google/gemini-3.8-flash": ((0.75, 3.75), None, None),
}
with open("report.txt", encoding="utf-8") as f:
    document = f.read()
messages = [{"role": "user", "content": "List the five key findings in this report:\n\n" + document}]

for model, (base, threshold, above) in PRICES.items():
    response = client.chat.completions.create(
        model=model, messages=messages, max_tokens=4096, reasoning_effort="low"
    )
    usage = response.usage
    inp, out = above if threshold and usage.prompt_tokens > threshold else base
    cost = (usage.prompt_tokens * inp + usage.completion_tokens * out) / 1_000_000
    print(model, usage.prompt_tokens, usage.completion_tokens, f"${cost:.6f}")

How do the long-prompt price thresholds work?

On Claude Haiku 5.5 the threshold is 100,000 tokens, and the prompt length includes tokens read from and written to the cache. When the prompt passes the threshold, input, cache and output all move to the higher price: $0.50 input, $0.05 cached input and $2.50 output, so the answer itself is also billed at five times the price. On GPT-6 Luna the threshold is 272,000 input tokens; on a request above it the input and cache prices double and the output price rises 1.5 times: $0.20, $0.02 and $0.75. On both models the tier is set for each request on its own.

MiniMax M3.1 Flash has no such threshold; $0.15 input, $0.015 cached input and $0.60 output apply across the whole 1,000,000-token window. Gemini 3.8 Flash's price does not change with prompt length but does depend on the date: $0.75 input, $0.075 cached input and $3.75 output through 31 December 2026, then $1.50, $0.15 and $7.50 from 1 January 2027. The switch happens on its own. If you have a flow that processes long documents in pieces, keeping the pieces under 100,000 tokens on Haiku 5.5 avoids the higher tier.

• Google, Gemini API pricing · 2026-10-19

Video, PDF, reasoning and sampling parameters

The two models whose cards list video input are MiniMax M3.1 Flash and Gemini 3.8 Flash; on MiniMax M3.1 Flash, send the video in a video_url part as a base64 data URL. Gemini 3.8 Flash also accepts audio and PDF, the GPT-6 Luna card lists PDF, and the Claude Haiku 5.5 card is limited to text and images. All four models take images in an image_url part, billed at the input price.

Reasoning control differs across the four models. On MiniMax M3.1 Flash reasoning cannot be turned off; reasoning_effort is chosen from low to max, and max applies when the field is omitted. Claude Haiku 5.5 and GPT-6 Luna accept six levels, default to medium, and none turns reasoning off. Gemini 3.8 Flash accepts low, medium and high and defaults to medium. A level that a model does not list returns 400; review this field as well when you change models.

Claude Haiku 5.5 and GPT-6 Luna do not support temperature and top_p. If you send these fields the request is not rejected; the fields are removed and appear in the llmtr_dropped_parameters list of the response. On Claude Haiku 5.5 the message list must end with a user message; an answer started with an assistant message is not accepted.

Trying all four with the same key

Open an account on llmtr.com, top up your balance under Dashboard > Billing and create a key under Dashboard > API Keys. Set the base URL in your OpenAI-compatible SDK to https://llmtr.com/v1. The same key works on all four models, you do not need a separate account with each of the four vendors, and each request is charged to your balance at the catalog price in force when it is processed.

Make the choice with an evaluation set from your own work: look at the length distribution of your prompts, work out the share of requests that pass the 100,000 and 272,000-token thresholds, and put the four models' answers side by side with the token counts in usage. Setting a spending limit on your API key caps the cost during the trial.

Comparing the four models with the same prompt

Send the same prompt to the four models with one LLMTR key, compare token counts and the cost for your prompt lengths, and choose by what you need.

1. Key. Top up your balance on llmtr.com and create a key under Dashboard > API Keys.
2. Same prompt. Send your own evaluation set with the four model ids to the https://llmtr.com/v1/chat/completions endpoint.
3. Choice. Compare the answers, token counts and cost for your prompt lengths, then choose the model id.

Frequently asked questions

How much do the four models cost?

Per 1M tokens, input / cached input / output: MiniMax M3.1 Flash $0.15 / $0.015 / $0.60; Claude Haiku 5.5 up to 100,000 tokens $0.10 / $0.01 / $0.50, above that $0.50 / $0.05 / $2.50; GPT-6 Luna up to 272,000 tokens $0.10 / $0.01 / $0.50, above that $0.20 / $0.02 / $0.75; Gemini 3.8 Flash through 31 December 2026 $0.75 / $0.075 / $3.75.

Which costs less on a long prompt?

On a prompt over 272,000 tokens, MiniMax M3.1 Flash: $0.15 input and $0.60 output, against GPT-6 Luna at $0.20 and $0.75 and Claude Haiku 5.5 at $0.50 and $2.50. For prompts between 100,000 and 272,000 tokens GPT-6 Luna costs less.

Which of them accept video?

MiniMax M3.1 Flash and Gemini 3.8 Flash. Gemini 3.8 Flash also accepts audio; the Claude Haiku 5.5 and GPT-6 Luna cards list no video.

Related posts

• MiniMax M3.1 Flash API: first request, reasoning_effort, price
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• Which MiniMax model? M3.1 Flash, M3, M2.7 and M2.5
• Mercury 3 vs Mercury 2.5 Preview vs Mercury 2
• Mercury 3 vs Claude Haiku 5.5, GPT-6 Luna, Gemini 3.8 Flash

• MiniMax M3.1 Flash model
• Claude Haiku 5.5 model
• GPT-6 Luna model
• Gemini 3.8 Flash model
• GPT-6 Sol and Luna documentation
• Google Gemini documentation