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Model comparison

GPT-OSS 120B vs MiniMax M2.7

Updated August 1, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

20 confirmed releases in the last 30 daysSee provider release alerts
GPT-OSS 120B

OpenAI

49.1/100

Supported · Public rank #124

90% interval 36.6–61.6

MiniMax M2.7

MiniMax

63.1/100

Supported · Public rank #40

90% interval 56.5–69.7

MiniMax M2.7 has the higher public score estimate, 63.09 versus 49.12, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

2 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Long documents

    Prompts that approach the documented context limit

    MiniMax M2.7

    MiniMax M2.7 has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GPT-OSS 120B does not fit this workload in one request. MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate. GPT-OSS 120B has no comparable published API token rate.

    Confidence: rate-fallback

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
2
GPT-OSS 120B only
0
MiniMax M2.7 only
16
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Not comparable
GPT-OSS 120B
Not measured
MiniMax M2.7
57.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Coding

Not comparable
GPT-OSS 120B
Not measured
MiniMax M2.7
53.3
Weighted basis
0 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-OSS 120B
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
GPT-OSS 120B
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
GPT-OSS 120B
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-OSS 120B
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GPT-OSS 120B
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-OSS 120B
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

GPT-OSS 120B
Self-hosted; infrastructure cost varies
Fits in one request
MiniMax M2.7
$0.0009
Fits in one request

GPT-OSS 120B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-OSS 120B
Self-hosted; infrastructure cost varies
Fits in one request
MiniMax M2.7
$0.0186
Fits in one request

GPT-OSS 120B has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

GPT-OSS 120B
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable
MiniMax M2.7
$0.078
Does not fit in one request
Cached input priced at the published list-input rate

GPT-OSS 120B does not fit this workload in one request. MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate. GPT-OSS 120B has no comparable published API token rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Context window

Maximum documented context; output-token limits may be lower.

GPT-OSS 120B

128K

MiniMax M2.7

200K

API model ID

GPT-OSS 120B

Not sourced

MiniMax M2.7

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

GPT-OSS 120B

No comparable hosted API rate

MiniMax M2.7

Not published

Documented inputs

GPT-OSS 120B

Not sourced

MiniMax M2.7

Not sourced

Documented outputs

GPT-OSS 120B

Not sourced

MiniMax M2.7

Not sourced

Provider availability

GPT-OSS 120B

Not sourced

MiniMax M2.7

Not sourced

Reasoning profile

GPT-OSS 120B

Non-Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

GPT-OSS 120B

Open Weight

MiniMax M2.7

Open Weight

License

GPT-OSS 120B

Open Weight

MiniMax M2.7

Open Weight

Release date

GPT-OSS 120B

2025-08-05

MiniMax M2.7

2026-03-18

If you already use one of these models
Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
MiniMax M2.7 has the higher public score estimate, 63.09 versus 49.12, but the 90% score intervals overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
MiniMax M2.7 has the larger documented window (200K).

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence18 rows

Agentic

  • Gert Labs

    Shared source
    GPT-OSS 120B29.61%
    MiniMax M2.740.40%

    MiniMax M2.7 leads this result

  • Terminal-Bench 2.0

    GPT-OSS 120B—
    MiniMax M2.757%
    Source

    Not directly comparable

  • Toolathlon

    GPT-OSS 120B—
    MiniMax M2.746.3%
    Source

    Not directly comparable

  • MLE-Bench Lite

    GPT-OSS 120B—
    MiniMax M2.766.6%
    Source

    Not directly comparable

  • MM-ClawBench

    GPT-OSS 120B—
    MiniMax M2.762.7%
    Source

    Not directly comparable

  • Claw-Eval

    GPT-OSS 120B—
    MiniMax M2.748.7%
    Source

    Not directly comparable

Coding

  • React Native Evals

    Shared source
    GPT-OSS 120B71.6%
    MiniMax M2.771.4%

    GPT-OSS 120B leads this result

  • SWE-bench Verified*

    GPT-OSS 120B—
    MiniMax M2.775.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    GPT-OSS 120B—
    MiniMax M2.756.2%
    Source

    Not directly comparable

  • SWE-Rebench

    GPT-OSS 120B—
    MiniMax M2.751.9%
    Source

    Not directly comparable

  • SWE Multilingual

    GPT-OSS 120B—
    MiniMax M2.776.5%
    Source

    Not directly comparable

  • Multi-SWE Bench

    GPT-OSS 120B—
    MiniMax M2.752.7%
    Source

    Not directly comparable

  • VIBE-Pro

    GPT-OSS 120B—
    MiniMax M2.755.6%
    Source

    Not directly comparable

  • NL2Repo

    GPT-OSS 120B—
    MiniMax M2.739.8%
    Source

    Not directly comparable

  • Vibe Code Bench

    GPT-OSS 120B—
    MiniMax M2.727.04%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    GPT-OSS 120B—
    MiniMax M2.787.0%
    Source

    Not directly comparable

  • MMLU-Pro (Arcee)

    GPT-OSS 120B—
    MiniMax M2.780.8%
    Source

    Not directly comparable

Math

  • AIME25 (Arcee)

    GPT-OSS 120B—
    MiniMax M2.780.0%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GPT-OSS 120B or MiniMax M2.7?

MiniMax M2.7 has the higher public score estimate, 63.09 versus 49.12, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, GPT-OSS 120B or MiniMax M2.7?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, GPT-OSS 120B or MiniMax M2.7?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, GPT-OSS 120B or MiniMax M2.7?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, GPT-OSS 120B or MiniMax M2.7?

MiniMax M2.7 has the larger documented context window: 200K, compared with 128K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 1, 2026

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