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BenchLM
OpenAI logo
Model A
GPT-5.5

OpenAI

73.27/100

Supported · Public rank #9

90% interval 71.0–75.6

GPT-5.5 vs MiniMax M2.7

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

MiniMax logo
Model B
MiniMax M2.7

MiniMax

58.98/100

Supported · Public rank #80

90% interval 51.6–66.3

Decision reading

GPT-5.5 has the higher public score, 73.27 versus 58.98, and the 90% score intervals do not overlap.

12 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-5.5

    GPT-5.5 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

    MiniMax M2.7 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

    MiniMax M2.7 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    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
12
GPT-5.5 only
26
MiniMax M2.7 only
11
Like-for-like categories
1 / 8

3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Knowledge

Like-for-like
GPT-5.5
73.3
Supported · #7/181
MiniMax M2.7
50.2
Supported · #88/181
Basis
BenchAlign lane · 6 vs 4 public rows
Reading
GPT-5.5 leads

Agentic

Directional only
GPT-5.5
63.9
Supported · #15/151
MiniMax M2.7
43.1
Estimated · #107/151
Basis
BenchAlign lane · 13 vs 7 public rows
Reading
Directional only

Coding

Directional only
GPT-5.5
67.7
Supported · #8/183
MiniMax M2.7
50.5
Estimated · #70/183
Basis
BenchAlign lane · 9 vs 11 public rows
Reading
Directional only

Instruction following

Directional only
GPT-5.5
92.9
#7/120
MiniMax M2.7
92.7
#10/120
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Reasoning

Not comparable
GPT-5.5
63.5
#15/22
MiniMax M2.7
75.2
Unranked · 2 rankable rows
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
GPT-5.5
69.6
Unranked · 3 rankable rows
MiniMax M2.7
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.5
Not ranked
MiniMax M2.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GPT-5.5
71.3
#19/48
MiniMax M2.7
Not ranked
Basis
Provisional lane · 2 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.

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.

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-5.5
$0.02
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-5.5
$0.34
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-5.5
$0.5
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.

Context window

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

MiniMax M2.7

200K

Cached-input rate

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

GPT-5.5

$0.5 per 1M cached input tokens

OpenAI pricing

MiniMax M2.7

Not published

Documented inputs

GPT-5.5

Not sourced

MiniMax M2.7

Not sourced

Documented outputs

GPT-5.5

Not sourced

MiniMax M2.7

Not sourced

Provider availability

GPT-5.5

Not sourced

MiniMax M2.7

Not sourced

Reasoning profile

GPT-5.5

Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

GPT-5.5

Proprietary

MiniMax M2.7

Open Weight

License

GPT-5.5

Proprietary

MiniMax M2.7

Open Weight

Release date

GPT-5.5

2026-04-23

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
GPT-5.5 has the higher public score, 73.27 versus 58.98, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.34 vs $0.0186. Cache-heavy agent loop: $0.5 vs $0.078.
Context tradeoff
GPT-5.5 has the larger documented window (1M).

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 evidence49 rows

Agentic

  • Terminal-Bench 2.0

    GPT-5.582%
    Source
    MiniMax M2.757%
    Source

    GPT-5.5 leads this result

  • CyberGym

    GPT-5.581.8%
    Source
    MiniMax M2.7—

    Not directly comparable

  • BrowseComp

    GPT-5.584.4%
    Source
    MiniMax M2.7—

    Not directly comparable

  • OSWorld-Verified

    GPT-5.578.7%
    Source
    MiniMax M2.7—

    Not directly comparable

  • MCP Atlas

    GPT-5.575.3%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Toolathlon

    GPT-5.555.6%
    Source
    MiniMax M2.746.3%
    Source

    GPT-5.5 leads this result

  • τ²-bench results

    GPT-5.598%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Gert Labs

    Shared source
    GPT-5.572.93%
    MiniMax M2.740.40%

    GPT-5.5 leads this result

  • ResearchClawBench

    GPT-5.517.0%
    Source
    MiniMax M2.7—

    Not directly comparable

  • OSWorld 2.0

    GPT-5.513.0%
    Source
    MiniMax M2.7—

    Not directly comparable

  • JobBench

    GPT-5.542.7%
    Source
    MiniMax M2.7—

    Not directly comparable

  • ExploitGym

    GPT-5.513.4%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    GPT-5.576.4%
    Source
    MiniMax M2.748.7%
    Source

    GPT-5.5 leads this result

  • MLE-Bench Lite

    GPT-5.5—
    MiniMax M2.766.6%
    Source

    Not directly comparable

  • MM-ClawBench

    GPT-5.5—
    MiniMax M2.762.7%
    Source

    Not directly comparable

  • Claw-Eval

    GPT-5.5—
    MiniMax M2.748.7%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    GPT-5.558.6%
    Source
    MiniMax M2.756.2%
    Source

    GPT-5.5 leads this result

  • Terminal-Bench 2.0

    GPT-5.582.0%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Vibe Code Bench

    Shared source
    GPT-5.569.85%
    MiniMax M2.727.04%

    GPT-5.5 leads this result

  • React Native Evals

    Shared source
    GPT-5.584.7%
    MiniMax M2.771.4%

    GPT-5.5 leads this result

  • cursorBench31

    GPT-5.559.2%
    Source
    MiniMax M2.7—

    Not directly comparable

  • cursorBench32

    GPT-5.558.4%
    Source
    MiniMax M2.7—

    Not directly comparable

  • FrontierCode 1.1 Main

    GPT-5.543.0%
    Source
    MiniMax M2.7—

    Not directly comparable

  • LiveCodeBench (Vals)

    GPT-5.585.3%
    Source
    MiniMax M2.779.9%
    Source

    GPT-5.5 leads this result

  • SWE-bench (Vals)

    GPT-5.582.6%
    Source
    MiniMax M2.773.8%
    Source

    GPT-5.5 leads this result

  • SWE-bench Verified*

    GPT-5.5—
    MiniMax M2.775.4%
    Source

    Not directly comparable

  • SWE-Rebench

    GPT-5.5—
    MiniMax M2.751.9%
    Source

    Not directly comparable

  • SWE Multilingual

    GPT-5.5—
    MiniMax M2.776.5%
    Source

    Not directly comparable

  • Multi-SWE Bench

    GPT-5.5—
    MiniMax M2.752.7%
    Source

    Not directly comparable

  • VIBE-Pro

    GPT-5.5—
    MiniMax M2.755.6%
    Source

    Not directly comparable

  • NL2Repo

    GPT-5.5—
    MiniMax M2.739.8%
    Source

    Not directly comparable

Reasoning

  • MRCR v2 64K-128K

    GPT-5.583.1%
    Source
    MiniMax M2.7—

    Not directly comparable

  • MRCR v2 128K-256K

    GPT-5.587.5%
    Source
    MiniMax M2.7—

    Not directly comparable

  • ARC-AGI-2

    GPT-5.585%
    Source
    MiniMax M2.7—

    Not directly comparable

  • ARC-AGI-3

    GPT-5.50.4%
    Source
    MiniMax M2.7—

    Not directly comparable

Knowledge

  • GPQA

    GPT-5.593.6%
    Source
    MiniMax M2.7—

    Not directly comparable

  • GPQA-D

    GPT-5.593.6%
    Source
    MiniMax M2.787.0%
    Source

    GPT-5.5 leads this result

  • HLE

    GPT-5.552.2%
    Source
    MiniMax M2.7—

    Not directly comparable

  • HLE w/o tools

    GPT-5.541.4%
    Source
    MiniMax M2.7—

    Not directly comparable

  • GPQA Diamond (Vals)

    GPT-5.593.2%
    Source
    MiniMax M2.786.6%
    Source

    GPT-5.5 leads this result

  • MMLU-Pro (Vals)

    GPT-5.588.1%
    Source
    MiniMax M2.780.4%
    Source

    GPT-5.5 leads this result

  • MMLU-Pro (Arcee)

    GPT-5.5—
    MiniMax M2.780.8%
    Source

    Not directly comparable

Math

  • FrontierMath (legacy)

    GPT-5.551.7%
    Source
    MiniMax M2.7—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    GPT-5.551.700%
    Source
    MiniMax M2.7—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-5.535.400%
    Source
    MiniMax M2.7—

    Not directly comparable

  • AIME25 (Arcee)

    GPT-5.5—
    MiniMax M2.780.0%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-5.581.2%
    Source
    MiniMax M2.7—

    Not directly comparable

  • MMMU-Pro w/ Python

    GPT-5.583.2%
    Source
    MiniMax M2.7—

    Not directly comparable

  • OfficeQA Pro

    GPT-5.554.1%
    Source
    MiniMax M2.7—

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.5 or MiniMax M2.7?

GPT-5.5 has the higher public score, 73.27 versus 58.98, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, GPT-5.5 or MiniMax M2.7?

GPT-5.5 scores higher for coding on the public lane, 67.7 to 50.5. MiniMax M2.7 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-5.5 or MiniMax M2.7?

GPT-5.5 scores higher for agentic tasks on the public lane, 63.9 to 43.1. MiniMax M2.7 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-5.5 or MiniMax M2.7?

For the stated presets, chat costs $0.02 on GPT-5.5 and $0.0009 on MiniMax M2.7; repository review costs $0.34 and $0.0186; the cache-heavy agent loop costs $0.5 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-5.5 or MiniMax M2.7?

GPT-5.5 has the larger documented context window: 1M, compared with 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 4, 2026

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