GPT-OSS 120B vs MiniMax M2.7

Updated October 29, 2026. Rank says MiniMax M2.7 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

MiniMax M2.7 has the higher public point estimate, 47.29 versus 40.74. Their conditional score ranges overlap. These ranges do not establish rank confidence. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

GPT-OSS 120B

OpenAI

40.74/100

Estimated · Public rank #136

Conditional range 31.0–50.5

Model B

MiniMax M2.7

MiniMax

47.29/100

Supported · Public rank #111

90% interval 35.2–59.4

Shared results: 3
GPT-OSS 120B only: 5
MiniMax M2.7 only: 26
Like-for-like categories: 1 / 8

Estimated: GPT-OSS 120B · Supported: MiniMax M2.7. Conditional ranges do not establish rank confidence.How the comparison works

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
GPT-OSS 120B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-OSS 120B is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

28.4GPT-OSS 120B34.7MiniMax M2.7

Directional only · BenchAlign v5.8

MiniMax M2.7 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

HLEKnowledge
Normalized gap 13.1
GPT-OSS 120B:14.9%
MiniMax M2.7:28%
GPT-OSS 120B source MiniMax M2.7 source

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Knowledge

Like-for-like

GPT-OSS 120B: 39.6; Supported · #116/179
MiniMax M2.7: 45.2; Supported · #94/179
Basis: BenchAlign v5.8 lane · 3 vs 5 public rows
Reading: MiniMax M2.7 leads · intervals overlap

Agentic

Directional only

GPT-OSS 120B: 27.5; Estimated · #114/135
MiniMax M2.7: 33.2; Supported · #99/135
Basis: BenchAlign v5.8 lane · 1 vs 8 public rows
Reading: Directional only

Coding

Directional only

GPT-OSS 120B: 28.4; Estimated · #108/156
MiniMax M2.7: 34.7; Supported · #85/156
Basis: BenchAlign v5.8 lane · 2 vs 12 public rows
Reading: Directional only

Instruction following

Directional only

GPT-OSS 120B: 84.3; #53/157
MiniMax M2.7: 93.0; #14/157
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

GPT-OSS 120B: 60.7; Unranked · 2 rankable rows
MiniMax M2.7: 78.8; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GPT-OSS 120B: Not ranked
MiniMax M2.7: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-OSS 120B: Not ranked
MiniMax M2.7: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-OSS 120B: Not ranked
MiniMax M2.7: 70.5; Unranked · 4 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-OSS 120B and MiniMax M2.7
Category	GPT-OSS 120B	MiniMax M2.7	Basis	Reading
Knowledge	39.6Supported · #116/179	45.2Supported · #94/179	Like-for-likeBenchAlign v5.8 lane · 3 vs 5 public rows	MiniMax M2.7 leads · intervals overlap
Agentic	27.5Estimated · #114/135	33.2Supported · #99/135	Directional onlyBenchAlign v5.8 lane · 1 vs 8 public rows	Directional only
Coding	28.4Estimated · #108/156	34.7Supported · #85/156	Directional onlyBenchAlign v5.8 lane · 2 vs 12 public rows	Directional only
Instruction following	84.3#53/157	93.0#14/157	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Reasoning	60.7Unranked · 2 rankable rows	78.8Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	70.5Unranked · 4 rankable rows	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

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

SpecificationGPT-OSS 120BMiniMax M2.7

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 point estimate, 47.29 versus 40.74. Their conditional score ranges overlap. These ranges do not establish rank confidence.
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.

Questions

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

MiniMax M2.7 has the higher public point estimate, 47.29 versus 40.74. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

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

MiniMax M2.7 scores higher for coding on the public lane, 34.7 to 28.4. GPT-OSS 120B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

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

MiniMax M2.7 scores higher for agentic tasks on the public lane, 33.2 to 27.5. GPT-OSS 120B is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

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.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

GPT-OSS 120B

API / mo$0

Self-host / mo$3,495

Break-even—

MiniMax M2.7

API / mo$1,125

Self-host / mo$6,990

Break-even409M/day

Model the full break-even

Benchmark evidence

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

Browse raw public benchmark evidence34 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
τ³-bench results
GPT-OSS 120B—
MiniMax M2.767.6%
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
Terminal-Bench 2.1 (Vals)
GPT-OSS 120B—
MiniMax M2.748.7%
Source
Not directly comparable

Coding

SWE-bench Verified
GPT-OSS 120B62.4%
Source
MiniMax M2.7—
Not directly comparable
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
Terminal-Bench 2.0
GPT-OSS 120B—
MiniMax M2.757.0%
Source
Not directly comparable
Vibe Code Bench
GPT-OSS 120B—
MiniMax M2.727.04%
Source
Not directly comparable
LiveCodeBench (Vals)
GPT-OSS 120B—
MiniMax M2.779.9%
Source
Not directly comparable
SWE-bench (Vals)
GPT-OSS 120B—
MiniMax M2.773.8%
Source
Not directly comparable

Knowledge

MMLU
GPT-OSS 120B90%
Source
MiniMax M2.7—
Not directly comparable
GPQA
GPT-OSS 120B80.1%
Source
MiniMax M2.7—
Not directly comparable
HLE
GPT-OSS 120B14.9%
Source
MiniMax M2.728%
Source
MiniMax M2.7 leads this result
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
GPQA Diamond (Vals)
GPT-OSS 120B—
MiniMax M2.786.6%
Source
Not directly comparable
MMLU-Pro (Vals)
GPT-OSS 120B—
MiniMax M2.780.4%
Source
Not directly comparable

Math

AIME 2024
GPT-OSS 120B95.8%
Source
MiniMax M2.7—
Not directly comparable
AIME 2025
GPT-OSS 120B92.5%
Source
MiniMax M2.7—
Not directly comparable
AIME25 (Arcee)
GPT-OSS 120B—
MiniMax M2.780.0%
Source
Not directly comparable
AIME26
GPT-OSS 120B—
MiniMax M2.789.8%
Source
Not directly comparable
HMMT Nov 2025
GPT-OSS 120B—
MiniMax M2.781.0%
Source
Not directly comparable
HMMT Feb 2026
GPT-OSS 120B—
MiniMax M2.772.7%
Source
Not directly comparable

34 public results · 3 shared

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Last updated October 29, 2026

GPT-OSS 120B vs MiniMax M2.7

Decision reading

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Knowledge

Agentic

Coding

Instruction following

Reasoning

Multimodal

Multilingual

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

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

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

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

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

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

Self-host vs API cost

Benchmark evidence

Agentic

Coding

Knowledge

Math

Related comparisons

Watch GPT-OSS 120B vs MiniMax M2.7