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BenchLM
OpenAI logo
Model A
GPT-5.4 Pro

OpenAI

61.53/100

Estimated · Public rank #55

90% interval 44.7–78.3

GPT-5.4 Pro vs MiniMax M3

Updated August 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

MiniMax logo
Model B
MiniMax M3

MiniMax

68.73/100

Supported · Public rank #21

90% interval 63.5–74.0

Decision reading

MiniMax M3 has the higher public score estimate, 68.73 versus 61.53, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

2 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-5.4 Pro

    GPT-5.4 Pro has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    MiniMax M3

    MiniMax M3 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

    MiniMax M3

    MiniMax M3 has the lower estimated token cost for this stated workload. GPT-5.4 Pro has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    MiniMax M3

    MiniMax M3 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

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

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

    Confidence: limited

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
2
GPT-5.4 Pro only
9
MiniMax M3 only
20
Like-for-like categories
0 / 8

2 categories use 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.

Agentic

Directional only
GPT-5.4 Pro
89.3
MiniMax M3
72.3
Weighted basis
1 vs 3 rows
Reading
Directional only

Multimodal

Directional only
GPT-5.4 Pro
94.0
MiniMax M3
64.9
Weighted basis
1 vs 2 rows
Reading
Directional only

Coding

Not comparable
GPT-5.4 Pro
Not measured
MiniMax M3
72.2
Weighted basis
0 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
GPT-5.4 Pro
83.3
MiniMax M3
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
GPT-5.4 Pro
58.7
MiniMax M3
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Math

Not comparable
GPT-5.4 Pro
46.9
MiniMax M3
85.7
Weighted basis
2 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
GPT-5.4 Pro
Not measured
MiniMax M3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GPT-5.4 Pro
Not measured
MiniMax M3
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-5.4 Pro
$0.12
Fits in one request
MiniMax M3
$0.0009
Fits in one request

MiniMax M3 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GPT-5.4 Pro
$2.04
Fits in one request
MiniMax M3
$0.0186
Fits in one request

MiniMax M3 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-5.4 Pro
$8.40
Fits in one request
Cached input priced at the published list-input rate
MiniMax M3
$0.03
Fits in one request

MiniMax M3 has the lower modeled cost

GPT-5.4 Pro 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.

Cached-input rate

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

GPT-5.4 Pro

Not published

OpenAI pricing

MiniMax M3

$0.06 per 1M cached input tokens

Provider availability

GPT-5.4 Pro

Generally Available · OpenAI Responses API

OpenAI model catalog

MiniMax M3

Not sourced

Reasoning profile

GPT-5.4 Pro

Reasoning

MiniMax M3

Non-Reasoning

Weight access

GPT-5.4 Pro

Proprietary

MiniMax M3

Open Weight

License

GPT-5.4 Pro

Proprietary

MiniMax M3

Open Weight

Release date

GPT-5.4 Pro

2026-03-05

MiniMax M3

2026-06-01

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 M3 has the higher public score estimate, 68.73 versus 61.53, but the 90% score intervals overlap.
Workload cost
Repository review: $2.04 vs $0.0186. Cache-heavy agent loop: $8.40 vs $0.03.
Context tradeoff
GPT-5.4 Pro has the larger documented window (1.05M).

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 evidence31 rows

Agentic

  • BrowseComp

    GPT-5.4 Pro89.3%
    Source
    MiniMax M383.5%
    Source

    GPT-5.4 Pro leads this result

  • Terminal-Bench 2.0

    GPT-5.4 Pro—
    MiniMax M366%
    Source

    Not directly comparable

  • OSWorld-Verified

    GPT-5.4 Pro—
    MiniMax M370.1%
    Source

    Not directly comparable

  • MCP Atlas

    GPT-5.4 Pro—
    MiniMax M374.2%
    Source

    Not directly comparable

  • Claw-Eval

    GPT-5.4 Pro—
    MiniMax M374.5%
    Source

    Not directly comparable

  • BankerToolBench

    GPT-5.4 Pro—
    MiniMax M376.1%
    Source

    Not directly comparable

  • ResearchClawBench

    GPT-5.4 Pro—
    MiniMax M319.8%
    Source

    Not directly comparable

  • OSWorld 2.0

    GPT-5.4 Pro—
    MiniMax M34.6%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    GPT-5.4 Pro—
    MiniMax M380.5%
    Source

    Not directly comparable

  • SWE-bench Pro

    GPT-5.4 Pro—
    MiniMax M359%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    GPT-5.4 Pro—
    MiniMax M366.0%
    Source

    Not directly comparable

  • NL2Repo

    GPT-5.4 Pro—
    MiniMax M342.1%
    Source

    Not directly comparable

  • VIBE V2

    GPT-5.4 Pro—
    MiniMax M350.1%
    Source

    Not directly comparable

  • SVG-Bench

    GPT-5.4 Pro—
    MiniMax M363.7%
    Source

    Not directly comparable

  • KernelBench Hard

    GPT-5.4 Pro—
    MiniMax M328.8%
    Source

    Not directly comparable

  • OpenHarmony Bench

    GPT-5.4 Pro—
    MiniMax M348.4%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    GPT-5.4 Pro83.3%
    Source
    MiniMax M3—

    Not directly comparable

Knowledge

  • HLE

    GPT-5.4 Pro58.7%
    Source
    MiniMax M3—

    Not directly comparable

  • FrontierScience

    GPT-5.4 Pro36.7%
    Source
    MiniMax M3—

    Not directly comparable

  • FrontierScience Research

    GPT-5.4 Pro36.7%
    Source
    MiniMax M3—

    Not directly comparable

  • HLE w/o tools

    GPT-5.4 Pro42.7%
    Source
    MiniMax M3—

    Not directly comparable

Math

  • IPhO 2025 (Theory)

    GPT-5.4 Pro93.5%
    Source
    MiniMax M3—

    Not directly comparable

  • FrontierMath (legacy)

    GPT-5.4 Pro50%
    Source
    MiniMax M3—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    GPT-5.4 Pro50.000%
    Source
    MiniMax M3—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-5.4 Pro37.500%
    Source
    MiniMax M3—

    Not directly comparable

  • USAMO 2026

    GPT-5.4 Pro—
    MiniMax M385.7%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    GPT-5.4 Pro94%
    Source
    MiniMax M378.1%
    Source

    GPT-5.4 Pro leads this result

  • OfficeQA Pro

    GPT-5.4 Pro—
    MiniMax M345.1%
    Source

    Not directly comparable

  • OmniDocBench 1.5

    GPT-5.4 Pro—
    MiniMax M391.6%
    Source

    Not directly comparable

  • VideoMMMU

    GPT-5.4 Pro—
    MiniMax M384.6%
    Source

    Not directly comparable

  • Video-MME (with subtitle)

    GPT-5.4 Pro—
    MiniMax M385.4%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GPT-5.4 Pro or MiniMax M3?

MiniMax M3 has the higher public score estimate, 68.73 versus 61.53, 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-5.4 Pro or MiniMax M3?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, GPT-5.4 Pro or MiniMax M3?

The current agentic tasks 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 costs less, GPT-5.4 Pro or MiniMax M3?

For the stated presets, chat costs $0.12 on GPT-5.4 Pro and $0.0009 on MiniMax M3; repository review costs $2.04 and $0.0186; the cache-heavy agent loop costs $8.40 and $0.03. GPT-5.4 Pro has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, GPT-5.4 Pro or MiniMax M3?

GPT-5.4 Pro has the larger documented context window: 1.05M, compared with 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 30, 2026

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