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GPT-5.5 vs MiniMax M2.7

Updated October 20, 2026. Rank says GPT-5.5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

GPT-5.5 has the higher public score, 69.22 versus 48.15, and the 90% score intervals do not overlap. 14 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
OpenAI logo
GPT-5.5

OpenAI

69.22/100

Supported · Public rank #20

90% interval 63.0–75.5

Model B
MiniMax logo
MiniMax M2.7

MiniMax

48.15/100

Supported · Public rank #107

90% interval 36.8–59.5

Shared results
14
GPT-5.5 only
30
MiniMax M2.7 only
15
Like-for-like categories
3 / 8
Supported: GPT-5.5 and MiniMax M2.7How 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.

  • Coding work

    Code generation, repair, and software-engineering tasks

    GPT-5.5

    GPT-5.5 has the higher public coding point estimate, 62 to 34.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

    Confidence: limited

  • Agentic work

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

    GPT-5.5

    GPT-5.5 has the higher public agentic point estimate, 62.7 to 32.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    GPT-5.5

    GPT-5.5 has the larger documented context window.

    Confidence: documented
Show secondary and unsupported calls

  • 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

  • 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

  • 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

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.

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

62.0GPT-5.534.4MiniMax M2.7

Like-for-like · BenchAlign v5.8

GPT-5.5 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 leaderboardShortlist models for this job
Same basis rules as the category table below

What is actually comparable

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

1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

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.

Agentic

Like-for-like
GPT-5.5
62.7
Supported · #23/131
MiniMax M2.7
32.3
Supported · #93/131
Basis
BenchAlign v5.8 lane · 15 vs 8 public rows
Reading
GPT-5.5 leads

Coding

Like-for-like
GPT-5.5
62.0
Supported · #14/152
MiniMax M2.7
34.4
Supported · #81/152
Basis
BenchAlign v5.8 lane · 10 vs 12 public rows
Reading
GPT-5.5 leads

Knowledge

Like-for-like
GPT-5.5
72.1
Supported · #15/187
MiniMax M2.7
46.0
Supported · #95/187
Basis
BenchAlign v5.8 lane · 6 vs 5 public rows
Reading
GPT-5.5 leads

Instruction following

Directional only
GPT-5.5
93.3
#8/149
MiniMax M2.7
93.0
#13/149
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Reasoning

Not comparable
GPT-5.5
70.3
#17/27
MiniMax M2.7
78.8
Unranked · 2 rankable rows
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GPT-5.5
75.5
#23/55
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

Math

Not comparable
GPT-5.5
91.2
Unranked · 3 rankable rows
MiniMax M2.7
70.5
Unranked · 4 rankable rows
Basis
Provisional lane · 2 vs 2 weighted rows
Reading
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-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.

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.

Context window

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

GPT-5.5

1M

OpenAI pricing

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, 69.22 versus 48.15, 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.

Questions

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

GPT-5.5 has the higher public score, 69.22 versus 48.15, 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 has the higher public coding point estimate, 62 to 34.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

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

GPT-5.5 has the higher public agentic tasks point estimate, 62.7 to 32.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

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.

Benchmark evidence

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

Browse raw public benchmark evidence59 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

  • ApprenticeBench

    GPT-5.520%
    Source
    MiniMax M2.7—

    Not directly comparable

  • MazeBench

    GPT-5.50%
    Source
    MiniMax M2.7—

    Not directly comparable

  • τ³-bench results

    GPT-5.5—
    MiniMax M2.767.6%
    Source

    Not directly comparable

  • 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 M2.757.0%
    Source

    GPT-5.5 leads this result

  • 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

  • CursorBench 3.2

    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

  • PostTrainBench v1.1

    GPT-5.527.2%
    Source
    MiniMax M2.7—

    Not directly comparable

  • 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

  • ARC-AGI-1

    GPT-5.595.00%
    Source
    MiniMax M2.7—

    Not directly comparable

Multimodal

  • ExtractBench (LlamaIndex)

    GPT-5.593.57%
    Source
    MiniMax M2.7—

    Not directly comparable

  • ParseBench

    GPT-5.567.76%
    Source
    MiniMax M2.7—

    Not directly comparable

  • 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

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 M2.728%
    Source

    GPT-5.5 leads this result

  • 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-5.585.263%
    Source
    MiniMax M2.7—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-5.572.500%
    Source
    MiniMax M2.7—

    Not directly comparable

  • AIME25 (Arcee)

    GPT-5.5—
    MiniMax M2.780.0%
    Source

    Not directly comparable

  • AIME26

    GPT-5.5—
    MiniMax M2.789.8%
    Source

    Not directly comparable

  • HMMT Nov 2025

    GPT-5.5—
    MiniMax M2.781.0%
    Source

    Not directly comparable

  • HMMT Feb 2026

    GPT-5.5—
    MiniMax M2.772.7%
    Source

    Not directly comparable

59 public results · 14 shared

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Last updated October 20, 2026