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minimax-m3

MiniMax M3

MiniMax-M3 is a multimodal foundation model from MiniMax. It supports text, image, and video inputs with text output, a 1M-token context window, and is suited for long-horizon agentic work, coding,...

5/31/2026
1,048,576 tokens
#67 Code (Overall)
Input: $0.2954/M
Output: $1.17/M

Specifications

Modalities

Input
text
image
video
Output
text

Supported Parameters

frequency_penalty
include_reasoning
logit_bias
logprobs
max_tokens
min_p
presence_penalty
reasoning
repetition_penalty
response_format
seed
stop
structured_outputs
temperature
tool_choice
tools
top_k
top_logprobs
top_p

Max Output Tokens

512,000

Data policy

Prompt retention
Unknown — we could not confirm
Training
Not used for training

"Unknown" does not mean "safe". It means we could not confirm it.

Whether a provider trains on prompts is a declared value from our terms with them. Retention is determined from the upstream listing for every host this model can reach. Neither is guessed.

Measured performance

Time to first response (p50)636ms
Throughput (p50)22 tokens/s

    Measured through FastMetal's own gateway over the last 30 days (603 first-response samples).

    Throughput is sampled once a day: the median of 25 observations over the last 30 days.

    Time to first response is measured to the first streamed chunk; throughput counts all output tokens, including reasoning tokens. These are observations, not a performance guarantee.

    Code Examples

    curl https://api.fastmetal.ai/v1/chat/completions \
      -H "Content-Type: application/json" \
      -H "Authorization: Bearer YOUR_API_KEY" \
      -d '{
        "model": "minimax-m3",
        "messages": [{"role": "user", "content": "Hello!"}]
      }'

    How MiniMax M3 actually answers

    Real responses to our standard prompts, recorded on FastMetal.

    Count the number of 'r's in 'strawberry'

    Count the number of 'r's in 'strawberry'. Explain your reasoning step by step.

    # Counting 'r's in "strawberry" Let me go through the word letter by letter: **s** - t - **r** - a - w - b - e - **r** - **r** - y | Position | Letter | Is it 'r'? | |----------|--------|------------| | 1 | s | No | | 2 | t | No | | 3 | **r** | ✓ (1st) | | 4 | a | No | | 5 | w | No | | 6 | b | No | | 7 | e | No | | 8 | **r** | ✓ (2nd) | | 9 | **r** | ✓ (3rd) | | 10 | y | No | ## Answer: **3** The word "strawberry" contains **three 'r's** — one at position 3, and two consecutive 'r's at positions 8 and 9.
    25.6s response455 output tokens

    Debug This Error

    I'm getting the following error in my Node.js application: TypeError: Cannot read properties of undefined (reading 'map') at UserList (/app/components/UserList.js:12:25) at renderWithHooks (/app/node_modules/rea…

    # Error Analysis The error `Cannot read properties of undefined (reading 'map')` tells you that **`users` is `undefined`** when your component runs. JavaScript can't call `.map()` on `undefined`, so it throws this error. The component itself is fine syntactically — the problem is that the `users` prop isn't being passed, or hasn't loaded yet. ## Common Causes **1. The prop isn't passed at all:** ```jsx <UserList /> // No users prop → users is undefined ``` **2. Data is still loading (async fetch):** ```jsx const [users, setUsers] = useState(); // undefined initially ``` **3. API returns undefined:** ```jsx const { data } = await fetch('/api/users').then(r => r.json()); setUsers(data);…
    32.8s response710 output tokens

    Code Review

    Please review the following Python function and suggest improvements for readability, performance, and best practices: def get_data(url, retries=3): import requests import time for i in range(retries):…

    ## Code Review This function works for the happy path, but it has several issues around error handling, retries, and readability. Here's a breakdown. ### 🐛 Bugs & Correctness Issues | # | Issue | Why It Matters | |---|-------|----------------| | 1 | `except:` with no exception type | Swallows `KeyboardInterrupt`, `SystemExit`, and *everything else* (typos, `MemoryError`). You lose visibility into real failures. | | 2 | Retries on every non‑200, including `4xx` | A `404` or `401` will never succeed by retrying — it just wastes time and rate-limit budget. | | 3 | `if item["active"] == True` | Both linters (PEP 8 E712) and Python idiom prefer `if item["active"]:`. | | 4 | `item["active"]` (…
    33.4s response3977 output tokens

    Compare these answers side by side with other models →

    Frequently asked questions

    How much does the MiniMax M3 API cost?
    On FastMetal, MiniMax M3 is billed per token: $0.2954 per 1M input tokens and $1.17 per 1M output tokens on a US-dollar account, before tax. Usage is drawn from a prepaid balance; there is no subscription or monthly fee.
    Can I call MiniMax M3 with the OpenAI SDK?
    Yes. Point base_url at https://api.fastmetal.ai/v1 and pass "minimax-m3" as the model; existing OpenAI-style code works unchanged, including streaming, tool calls and structured output.
    What is the context window of MiniMax M3?
    1,048,576 tokens, shared between the prompt and the response.
    How does MiniMax M3 rank?
    #96 on the public arena's Japanese board (ELO 1,392). Ranks move as the leaderboard is updated.
    What do I need to try MiniMax M3?
    Create an account and add credit; the model is then available both in the browser chat and over the API. There is no contract or minimum spend.

    Try MiniMax M3 right now

    MiniMax M3 is available on FastMetal through one API key. Start in the browser, or call it from the OpenAI SDK.

    Leaderboard

    Text
    russianELO: 1,437
    #97
    CodingELO: 1,496
    #88
    MathELO: 1,430
    #110
    Creative WritingELO: 1,404
    #104
    Instruction FollowingELO: 1,434
    #91
    Hard PromptsELO: 1,463
    #91
    Multi-TurnELO: 1,452
    #85
    OverallELO: 1,440
    #97
    JapaneseELO: 1,392
    #96
    ChineseELO: 1,473
    #106
    KoreanELO: 1,370
    #117
    EnglishELO: 1,452
    #90
    frenchELO: 1,455
    #103
    germanELO: 1,438
    #92
    spanishELO: 1,430
    #109
    Vision
    OverallELO: 1,233
    #72
    Code
    OverallELO: 1,482
    #67

    Rankings: LMArena leaderboard dataset (lmarena-ai/leaderboard-dataset), CC BY 4.0, snapshot 2026-10-22; category names mapped and ratings rounded by FastMetal.