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Choosing a model

Ten models, one base URL. Most people only ever need three of them. This page tells you which one to put in the model field, and warns you about the one setting that silently breaks reasoning models.

The short answer

Your job	Model	Why
Agentic coding, long tool chains	minimax-m3	The flagship. Built to hold a long plan together.
Everyday coding, edits, review	minimax-m2.7	Balanced default. Good enough for most of the day.
High-volume, batch, background	minimax-m2.5	Tuned for speed and volume. Terser, so less waiting.
Latency-sensitive, interactive UI	minimax-m2.7-highspeed	Same weights, latency-tuned serving. Costs 2x per token.
Very large documents	minimax-text-01	4M context. Cheapest per token in the catalog.
Role-play, character chat	minimax-m2-her	Dialogue tuning. Do not wire it into a coding agent.
Legacy configs	minimax-m2.1, minimax-m2	Older generations, kept so old configs keep working.

If you are wiring up a coding agent and you do not want to think about it: set the main model to minimax-m3 and the background or "fast" model to minimax-m2.5. That is what every agent guide in these docs does.

Cost is not the reason to pick a smaller model

Read the rate card before you optimise. Every standard M-series model meters at the same rate: $0.30 per 1M input, $1.20 per 1M output. So minimax-m2.5 is not cheaper per token than minimax-m3. Picking it saves you money only because it tends to be terser.

Two things actually move your bill:

• Highspeed variants cost exactly 2x. Use them when time-to-first-token matters. Never use them for background or batch work.
• Tokens. Trimming context and capping output length saves far more than swapping models does.

minimax-text-01 is the one genuine price break: $0.20 in, $1.10 out.

The max_tokens gotcha

Read this one. It is the failure mode most likely to waste your first hour.

The M-series are reasoning models. Before they emit an answer they burn tokens on internal reasoning, and that reasoning counts against max_tokens. Set max_tokens too low and the whole budget gets eaten by thinking. The request succeeds, returns HTTP 200, bills you for the tokens, and hands back empty content.

Code
# Broken. 150 tokens is not enough for a reasoning model to think AND answer.
resp = client.chat.completions.create(
    model="minimax-m3",
    messages=[{"role": "user", "content": "What is a Bloom filter?"}],
    max_tokens=150,
)
print(resp.choices[0].message.content)   # ""  <- empty, and you paid for it

Code
# Fixed. Give it room.
resp = client.chat.completions.create(
    model="minimax-m3",
    messages=[{"role": "user", "content": "What is a Bloom filter?"}],
    max_tokens=2000,
)
print(resp.choices[0].message.content)   # a real answer

Rules of thumb:

• Chat-style calls: max_tokens of 2000 or more. A few hundred is not safe.
• Agent loops and tool calling: 4000 or more. Reasoning plus a tool call argument blob adds up.
• Never use max_tokens as a cost control. It does not shorten the reasoning, it just truncates the answer. If you want short output, say so in the prompt.
• Empty content with finish_reason: "length" means you hit this. Raise max_tokens and try again.

The same applies on the Anthropic surface, where max_tokens is a required field. Do not pass a small number there either.

Code
resp = client.messages.create(
    model="minimax-m3",
    max_tokens=2000,          # required, and 2000 is the floor you want
    messages=[{"role": "user", "content": "What is a Bloom filter?"}],
)

Context windows

The standard M-series models carry about 205k tokens of context. That is enough for a large repo slice or a long agent session. minimax-m2-her is smaller at 66k. minimax-text-01 is the outlier at 4M, which is why it is the model for whole-book and whole-corpus work.

Context is not free. Every token you send is billed on every turn of a conversation. A long agent session re-sends its whole history each call, so context length compounds. Prune it.

What the models cannot do

The catalog is text in, text out. There are no embedding models, no image models, and no audio models on the gateway. If your pipeline needs embeddings, run them somewhere else and send the text results here.

Switching models mid-project

Model IDs are real provider names, not aliases. minimax-m3 will always mean MiniMax M3. When the catalog changes we announce it first, and GET /v1/models always returns what your key can reach right now.

During a provider incident a request may be served by a smaller model of the same family rather than failing. The model field in the response reports what actually served the request. See Models for the fallback ladders.

Related

• Models: the full catalog and rate card.
• Recipes: working code for each surface.
• Rate limits & caps: what happens at the monthly cap.