Skip to main content
BenchLM
Advertise

Model comparison

GPT-5.2 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-5.2

OpenAI

57.6/100

Estimated · Public rank #72

90% interval 49.3–65.9

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 57.61, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

3 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

    GPT-5.2

    GPT-5.2 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

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    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

  • 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. GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate. 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
3
GPT-5.2 only
12
MiniMax M2.7 only
15
Like-for-like categories
0 / 8

1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Coding

Directional only
GPT-5.2
70.6
MiniMax M2.7
53.3
Weighted basis
2 vs 2 rows
Reading
Directional only

Agentic

Not comparable
GPT-5.2
55.7
MiniMax M2.7
57.0
Weighted basis
2 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-5.2
52.9
MiniMax M2.7
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
GPT-5.2
92.4
MiniMax M2.7
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Math

Not comparable
GPT-5.2
35.2
MiniMax M2.7
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.2
Not measured
MiniMax M2.7
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GPT-5.2
80.4
MiniMax M2.7
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.2
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.

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-5.2
$0.00875
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-5.2
$0.1295
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-5.2
$0.525
Fits in one request
Cached input priced at the published list-input rate
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. GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate. 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.

GPT-5.2

400K

MiniMax M2.7

200K

API model ID

GPT-5.2

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-5.2

Not published

MiniMax M2.7

Not published

Documented inputs

GPT-5.2

Not sourced

MiniMax M2.7

Not sourced

Documented outputs

GPT-5.2

Not sourced

MiniMax M2.7

Not sourced

Provider availability

GPT-5.2

Not sourced

MiniMax M2.7

Not sourced

Reasoning profile

GPT-5.2

Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

GPT-5.2

Proprietary

MiniMax M2.7

Open Weight

License

GPT-5.2

Proprietary

MiniMax M2.7

Open Weight

Release date

GPT-5.2

2025-12-11

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 57.61, but the 90% score intervals overlap.
Workload cost
Repository review: $0.1295 vs $0.0186. Cache-heavy agent loop: $0.525 vs $0.078.
Context tradeoff
GPT-5.2 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 evidence30 rows

Agentic

  • BrowseComp

    GPT-5.265.8%
    Source
    MiniMax M2.7—

    Not directly comparable

  • OSWorld-Verified

    GPT-5.247.3%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Gert Labs

    Shared source
    GPT-5.246.54%
    MiniMax M2.740.40%

    GPT-5.2 leads this result

  • JobBench

    GPT-5.234.3%
    Source
    MiniMax M2.7—

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-5.2—
    MiniMax M2.757%
    Source

    Not directly comparable

  • Toolathlon

    GPT-5.2—
    MiniMax M2.746.3%
    Source

    Not directly comparable

  • MLE-Bench Lite

    GPT-5.2—
    MiniMax M2.766.6%
    Source

    Not directly comparable

  • MM-ClawBench

    GPT-5.2—
    MiniMax M2.762.7%
    Source

    Not directly comparable

  • Claw-Eval

    GPT-5.2—
    MiniMax M2.748.7%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    GPT-5.280%
    Source
    MiniMax M2.7—

    Not directly comparable

  • SWE-bench Pro

    GPT-5.255.6%
    Source
    MiniMax M2.756.2%
    Source

    MiniMax M2.7 leads this result

  • Vibe Code Bench

    Shared source
    GPT-5.253.50%
    MiniMax M2.727.04%

    GPT-5.2 leads this result

  • SWE-bench Verified*

    GPT-5.2—
    MiniMax M2.775.4%
    Source

    Not directly comparable

  • SWE-Rebench

    GPT-5.2—
    MiniMax M2.751.9%
    Source

    Not directly comparable

  • SWE Multilingual

    GPT-5.2—
    MiniMax M2.776.5%
    Source

    Not directly comparable

  • Multi-SWE Bench

    GPT-5.2—
    MiniMax M2.752.7%
    Source

    Not directly comparable

  • VIBE-Pro

    GPT-5.2—
    MiniMax M2.755.6%
    Source

    Not directly comparable

  • NL2Repo

    GPT-5.2—
    MiniMax M2.739.8%
    Source

    Not directly comparable

  • React Native Evals

    GPT-5.2—
    MiniMax M2.771.4%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GPT-5.252.9%
    Source
    MiniMax M2.7—

    Not directly comparable

Knowledge

  • GPQA

    GPT-5.292.4%
    Source
    MiniMax M2.7—

    Not directly comparable

  • GPQA-D

    GPT-5.2—
    MiniMax M2.787.0%
    Source

    Not directly comparable

  • MMLU-Pro (Arcee)

    GPT-5.2—
    MiniMax M2.780.8%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    GPT-5.240.700%
    Source
    MiniMax M2.7—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-5.218.800%
    Source
    MiniMax M2.7—

    Not directly comparable

  • AIME25 (Arcee)

    GPT-5.2—
    MiniMax M2.780.0%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-5.279.5%
    Source
    MiniMax M2.7—

    Not directly comparable

  • MathVision

    GPT-5.283.0%
    Source
    MiniMax M2.7—

    Not directly comparable

  • CharXiv

    GPT-5.282.1%
    Source
    MiniMax M2.7—

    Not directly comparable

  • V*

    GPT-5.275.9%
    Source
    MiniMax M2.7—

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.2 or MiniMax M2.7?

MiniMax M2.7 has the higher public score estimate, 63.09 versus 57.61, 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-5.2 or MiniMax M2.7?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, GPT-5.2 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-5.2 or MiniMax M2.7?

For the stated presets, chat costs $0.00875 on GPT-5.2 and $0.0009 on MiniMax M2.7; repository review costs $0.1295 and $0.0186; the cache-heavy agent loop costs $0.525 and $0.078. MiniMax M2.7 does not fit this workload in one request. GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GPT-5.2 or MiniMax M2.7?

GPT-5.2 has the larger documented context window: 400K, compared with 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 1, 2026

Watch GPT-5.2 vs MiniMax M2.7

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 2,000+ readers.