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BenchLM
OpenAI logo
Model A
GPT-5.1-Codex-Max

OpenAI

52.38/100

Estimated · Public rank #113

90% interval 40.9–63.9

GPT-5.1-Codex-Max vs MiniMax M2.7

Updated September 15, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

MiniMax logo
Model B
MiniMax M2.7

MiniMax

55.14/100

Supported · Public rank #95

90% interval 43.8–66.5

Decision reading

MiniMax M2.7 has the higher public score estimate, 55.14 versus 52.38, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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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

    GPT-5.1-Codex-Max

    GPT-5.1-Codex-Max has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    MiniMax M2.7

    MiniMax M2.7 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    MiniMax M2.7

    MiniMax M2.7 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    GPT-5.1-Codex-Max is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    GPT-5.1-Codex-Max is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

  • 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. 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.

    Confidence: rate-fallback

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
1
GPT-5.1-Codex-Max only
0
MiniMax M2.7 only
22
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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.

Agentic

Not comparable
GPT-5.1-Codex-Max
Not ranked
MiniMax M2.7
41.1
Estimated · #110/153
Basis
BenchAlign lane · 0 vs 7 public rows
Reading
Not comparable

Coding

Not comparable
GPT-5.1-Codex-Max
Not ranked
MiniMax M2.7
48.6
Estimated · #68/152
Basis
BenchAlign lane · 1 vs 11 public rows
Reading
Not comparable

Reasoning

Not comparable
GPT-5.1-Codex-Max
Not ranked
MiniMax M2.7
74.8
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
GPT-5.1-Codex-Max
Not ranked
MiniMax M2.7
48.7
Supported · #90/183
Basis
BenchAlign lane · 0 vs 4 public rows
Reading
Not comparable

Math

Not comparable
GPT-5.1-Codex-Max
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.1-Codex-Max
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GPT-5.1-Codex-Max
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.1-Codex-Max
Not ranked
MiniMax M2.7
93.0
#10/123
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

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

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-5.1-Codex-Max
$0.00625
Fits in one request
MiniMax M2.7
$0.0009
Fits in one request

MiniMax M2.7 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GPT-5.1-Codex-Max
$0.0925
Fits in one request
MiniMax M2.7
$0.0186
Fits in one request

MiniMax M2.7 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

GPT-5.1-Codex-Max
$0.15
Fits in one request
MiniMax M2.7
$0.078
Does not fit in one request
Cached input priced at the published list-input rate

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.

Specification differences

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

API model ID

GPT-5.1-Codex-Max

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-5.1-Codex-Max

$0.125 per 1M cached input tokens

OpenAI GPT-5.1-Codex-Max model documentation

MiniMax M2.7

Not published

Documented inputs

GPT-5.1-Codex-Max

Not sourced

MiniMax M2.7

Not sourced

Documented outputs

GPT-5.1-Codex-Max

Not sourced

MiniMax M2.7

Not sourced

Provider availability

GPT-5.1-Codex-Max

Not sourced

MiniMax M2.7

Not sourced

Reasoning profile

GPT-5.1-Codex-Max

Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

GPT-5.1-Codex-Max

Proprietary

MiniMax M2.7

Open Weight

License

GPT-5.1-Codex-Max

Proprietary

MiniMax M2.7

Open Weight

Release date

GPT-5.1-Codex-Max

2025-11-19

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, 55.14 versus 52.38, but the 90% score intervals overlap.
Workload cost
Repository review: $0.0925 vs $0.0186. Cache-heavy agent loop: $0.15 vs $0.078.
Context tradeoff
GPT-5.1-Codex-Max has the larger documented window (400K).

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 evidence23 rows

Agentic

  • Terminal-Bench 2.0

    GPT-5.1-Codex-Max—
    MiniMax M2.757%
    Source

    Not directly comparable

  • Toolathlon

    GPT-5.1-Codex-Max—
    MiniMax M2.746.3%
    Source

    Not directly comparable

  • MLE-Bench Lite

    GPT-5.1-Codex-Max—
    MiniMax M2.766.6%
    Source

    Not directly comparable

  • MM-ClawBench

    GPT-5.1-Codex-Max—
    MiniMax M2.762.7%
    Source

    Not directly comparable

  • Claw-Eval

    GPT-5.1-Codex-Max—
    MiniMax M2.748.7%
    Source

    Not directly comparable

  • Gert Labs

    GPT-5.1-Codex-Max—
    MiniMax M2.740.40%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    GPT-5.1-Codex-Max—
    MiniMax M2.748.7%
    Source

    Not directly comparable

Coding

  • Vibe Code Bench

    Shared source
    GPT-5.1-Codex-Max22.17%
    MiniMax M2.727.04%

    MiniMax M2.7 leads this result

  • SWE-bench Verified*

    GPT-5.1-Codex-Max—
    MiniMax M2.775.4%
    Source

    Not directly comparable

  • SWE-bench Pro

    GPT-5.1-Codex-Max—
    MiniMax M2.756.2%
    Source

    Not directly comparable

  • SWE-Rebench

    GPT-5.1-Codex-Max—
    MiniMax M2.751.9%
    Source

    Not directly comparable

  • SWE Multilingual

    GPT-5.1-Codex-Max—
    MiniMax M2.776.5%
    Source

    Not directly comparable

  • Multi-SWE Bench

    GPT-5.1-Codex-Max—
    MiniMax M2.752.7%
    Source

    Not directly comparable

  • VIBE-Pro

    GPT-5.1-Codex-Max—
    MiniMax M2.755.6%
    Source

    Not directly comparable

  • NL2Repo

    GPT-5.1-Codex-Max—
    MiniMax M2.739.8%
    Source

    Not directly comparable

  • React Native Evals

    GPT-5.1-Codex-Max—
    MiniMax M2.771.4%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    GPT-5.1-Codex-Max—
    MiniMax M2.779.9%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    GPT-5.1-Codex-Max—
    MiniMax M2.773.8%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    GPT-5.1-Codex-Max—
    MiniMax M2.787.0%
    Source

    Not directly comparable

  • MMLU-Pro (Arcee)

    GPT-5.1-Codex-Max—
    MiniMax M2.780.8%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    GPT-5.1-Codex-Max—
    MiniMax M2.786.6%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    GPT-5.1-Codex-Max—
    MiniMax M2.780.4%
    Source

    Not directly comparable

Math

  • AIME25 (Arcee)

    GPT-5.1-Codex-Max—
    MiniMax M2.780.0%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.1-Codex-Max or MiniMax M2.7?

MiniMax M2.7 has the higher public score estimate, 55.14 versus 52.38, 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-5.1-Codex-Max or MiniMax M2.7?

GPT-5.1-Codex-Max is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, GPT-5.1-Codex-Max or MiniMax M2.7?

GPT-5.1-Codex-Max is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, GPT-5.1-Codex-Max or MiniMax M2.7?

For the stated presets, chat costs $0.00625 on GPT-5.1-Codex-Max and $0.0009 on MiniMax M2.7; repository review costs $0.0925 and $0.0186; the cache-heavy agent loop costs $0.15 and $0.078. 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.

Which has the larger context window, GPT-5.1-Codex-Max or MiniMax M2.7?

GPT-5.1-Codex-Max has the larger documented context window: 400K, compared with 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 15, 2026

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