MiniMax · Mid-tier · 200K context window

MiniMax M2 pricing for code review

$0.003300 per call at 5,000 input / 2,000 output tokens.

Per call

$0.003300

10K calls/mo

$33

Cache savings

0% off input

Batch savings

0% off all

Calculate your MiniMax M2 costs

Adjust token counts, volume, and discount toggles to model your code review spend. Prices verified daily against provider pricing pages.

Configure your usage

5,000
100100K
2,000
5050K
10,000
1001M

Estimated cost

Monthly cost
$33
10,000 calls/month
Cost per call$0.003300
Daily cost$1
Input rate$0.26/MTok
Output rate$1.00/MTok
Analyze your real spend

Quick start

Call MiniMax M2 with the OpenAI Python SDK pointed at api.minimax.chat:

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.minimax.chat/v1",
    api_key=os.environ["MINIMAX_API_KEY"],
)
response = client.chat.completions.create(
    model="minimax-m2",
    max_tokens=2000,
    messages=[{"role": "user", "content": "Your prompt here"}]
)
# Cost: ~$0.003300 per call at standard rates
# Input:  $0.26/MTok
# Output: $1.00/MTok

How MiniMax M2 compares on cost

Per-call cost for all models scoring 50+ on code review benchmarks. Hover for details.

Highest-quality affordable option: Claude Sonnet 5 at $0.0300/call (9.1x the cost, 96/100 quality). MiniMax M2 is already the cheapest choice for code review.

What drives code review cost

Review is input-heavy in a way generation is not: the diff or file goes in whole, and structured feedback comes back short. That flips the usual optimization: input rate and cache discounts matter more than output rate. Reviewing the same repository repeatedly means the surrounding context is identical call over call, which is exactly the shape prompt caching is designed for.

For MiniMax M2 specifically, that means output tokens cost 3.8x more than input, at $1.00 vs $0.26 per MTok. At this use case’s 5,000/2,000 token split, output accounts for 61% of the $0.003300 per-call cost.

Complete MiniMax M2 pricing breakdown

Standard token rates

Direction$/MTokTokens per call
Input$0.265,000
Output$1.002,000

Batch API: 0% off all tokens

Submit requests asynchronously and receive 0% off all token costs. This works best for offline pipelines where latency requirements are flexible.

Monthly cost projections

Monthly volumeCost per callStandard monthlyCached monthlyYou save with caching
10,000 calls$0.003300$33$33$0.0000
50,000 calls$0.003300$165$165$0.0000
100,000 calls$0.003300$330$330$0.0000
500,000 calls$0.003300$1,650$1,650$0.0000
1,000,000 calls$0.003300$3,300$3,300$0.0000

MiniMax M2 quality benchmarks for code review

Benchmark data for code review is pending for MiniMax M2. Test against your own evaluation suite before moving to production.

Benchmark data is not yet available for MiniMax M2. We recommend testing against your own evaluation criteria.

Alternative models for code review

6 models score 80+ on code review benchmarks, sorted by input rate. Cheapest: Claude Sonnet 5 (809% more, 96/100 quality).

ModelProviderInput $/MTokOutput $/MTokQualityPer callvs MiniMax
Claude Sonnet 5Claude$2.00$10.0096/100$0.0300809% more
Claude Sonnet 4.6Claude$3.00$15.0095/100$0.04501264% more
GPT 5.6 SolGPT$4.00$20.0092/100$0.06001718% more
Claude Opus 4.8Claude$5.00$25.0094/100$0.07502173% more
GPT 5.5GPT$5.00$30.0090/100$0.08502476% more
Claude Fable 5Claude$10.00$50.0095/100$0.15004445% more

About MiniMax M2

Provider

MiniMax

Tier

Mid-tier

Context

200K tokens

Released

Not published

Prices from platform.minimax.io/docs/pricing/overview (read 2026-10-11). No cache discount published (set 1.0 — no discount). No batch discount published. Not yet benchmarked, so never recommended.

Other models from MiniMax

ModelTierInput $/MTokOutput $/MTokContext
MiniMax M3Flagship$0.30$1.201M

You can compare MiniMax M2 head-to-head against any other model on our model comparison pages, or track how pricing has changed over time on the pricing history tracker.

Frequently asked questions

How much does MiniMax M2 cost for code review per API call?

A typical code review call using MiniMax M2 costs about $0.003300 per call. This estimate uses 5,000 input tokens and 2,000 output tokens at standard rates. Standard rates are $0.26 per million input tokens and $1.00 per million output tokens.

Is MiniMax M2 the best model for code review?

Our benchmark data does not yet include a code review ranking for MiniMax M2. We recommend testing it against your own evaluation criteria before production usage.

What is the cheapest model for code review with good quality?

The most affordable model for code review above 80/100 quality is Claude Sonnet 5. It comes from Claude at $2.00 per million input tokens. Output costs $10.00 per million tokens with a quality score of 96/100. Claude Sonnet 5 costs more per call than MiniMax M2 but scores higher on quality benchmarks for this task type.

How can I reduce my MiniMax M2 costs for code review?

Three strategies lower MiniMax M2 costs for this task type effectively. Prompt caching gives a 0% discount on input tokens when reusing system prompts. The batch API gives 0% off all tokens for asynchronous processing workloads. Prompt optimization reduces token counts through more concise instructions and context windows. Combining caching with batching brings your cost from $0.003300 to about $0.003300 per call.

How does MiniMax M2 pricing compare to other MiniMax models?

MiniMax has 1 other models alongside MiniMax M2 today: MiniMax M3 at $0.30/$1.20 per million tokens (flagship). The right choice depends on whether your code review workload prioritizes quality, cost, or speed.

What context window does MiniMax M2 support?

MiniMax M2 supports a context window of 200K tokens per API call. This sets the maximum combined length of your input prompt and generated response. For code review, typical calls use 5,000 input and 2,000 output tokens. That is well within this limit, leaving room for longer prompts or multi-turn conversations.

Track what you spend on AI

This calculator estimates costs based on published rates only. Connect your API usage to see where your money goes and which models drive your spend.