GPT-5.4 vs MiniMax M2.7

Updated September 29, 2026. Rank says GPT-5.4 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

GPT-5.4 has the higher public score, 68.22 versus 47.85, and the 90% score intervals do not overlap. 8 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

GPT-5.4

OpenAI

68.22/100

Supported · Public rank #21

90% interval 64.3–72.2

Model B

MiniMax M2.7

MiniMax

47.85/100

Supported · Public rank #89

90% interval 36.9–58.9

Shared results: 8
GPT-5.4 only: 30
MiniMax M2.7 only: 15
Like-for-like categories: 3 / 8

Supported: GPT-5.4 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-5.4
GPT-5.4 leads on the public coding lane, 49.8 to 36, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
GPT-5.4
GPT-5.4 leads on the public agentic lane, 49.5 to 29.1, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Long documents
Prompts that approach the documented context limit
GPT-5.4
GPT-5.4 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.

Pick the job you would use them for

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

49.8GPT-5.436.0MiniMax M2.7

Like-for-like · BenchAlign v5.7

GPT-5.4 leads the like-for-like coding row, although the 90% intervals overlap.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

Terminal-Bench 2.0Agentic
Normalized gap 18.1
GPT-5.4:75.1%
MiniMax M2.7:57%
GPT-5.4 source MiniMax M2.7 source
SWE-bench ProCoding
Normalized gap 1.5
GPT-5.4:57.7%
MiniMax M2.7:56.2%
GPT-5.4 source MiniMax M2.7 source

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 v5.7 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-5.4: 49.5; Supported · #40/117
MiniMax M2.7: 29.1; Supported · #82/117
Basis: BenchAlign v5.7 lane · 14 vs 7 public rows
Reading: GPT-5.4 leads

Coding

Like-for-like

GPT-5.4: 49.8; Supported · #41/143
MiniMax M2.7: 36.0; Supported · #77/143
Basis: BenchAlign v5.7 lane · 4 vs 11 public rows
Reading: GPT-5.4 leads · intervals overlap

Knowledge

Like-for-like

GPT-5.4: 66.3; Supported · #20/169
MiniMax M2.7: 43.0; Supported · #86/169
Basis: BenchAlign v5.7 lane · 7 vs 4 public rows
Reading: GPT-5.4 leads

Instruction following

Directional only

GPT-5.4: 89.2; #19/124
MiniMax M2.7: 91.6; #10/124
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Reasoning

Not comparable

GPT-5.4: 60.6; #21/27
MiniMax M2.7: 76.1; Unranked · 2 rankable rows
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GPT-5.4: 69.3; #21/50
MiniMax M2.7: Not ranked
Basis: Provisional lane · 3 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.4: Not ranked
MiniMax M2.7: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-5.4: 64.4; Unranked · 2 rankable rows
MiniMax M2.7: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-5.4 and MiniMax M2.7
Category	GPT-5.4	MiniMax M2.7	Basis	Reading
Agentic	49.5Supported · #40/117	29.1Supported · #82/117	Like-for-likeBenchAlign v5.7 lane · 14 vs 7 public rows	GPT-5.4 leads
Coding	49.8Supported · #41/143	36.0Supported · #77/143	Like-for-likeBenchAlign v5.7 lane · 4 vs 11 public rows	GPT-5.4 leads · intervals overlap
Knowledge	66.3Supported · #20/169	43.0Supported · #86/169	Like-for-likeBenchAlign v5.7 lane · 7 vs 4 public rows	GPT-5.4 leads
Instruction following	89.2#19/124	91.6#10/124	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Reasoning	60.6#21/27	76.1Unranked · 2 rankable rows	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable
Multimodal	69.3#21/50	Not ranked	Not comparableProvisional lane · 3 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	64.4Unranked · 2 rankable rows	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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-5.4: $0.01; 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-5.4: $0.17; 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-5.4: $0.25; 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.

SpecificationGPT-5.4MiniMax M2.7

Context window

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

GPT-5.4

1.05M

OpenAI pricing

MiniMax M2.7

200K

API model ID

GPT-5.4

gpt-5.4

OpenAI pricing

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-5.4

$0.25 per 1M cached input tokens

OpenAI pricing

MiniMax M2.7

Not published

Documented inputs

GPT-5.4

Not sourced

MiniMax M2.7

Not sourced

Documented outputs

GPT-5.4

Not sourced

MiniMax M2.7

Not sourced

Provider availability

GPT-5.4

Not sourced

MiniMax M2.7

Not sourced

Reasoning profile

GPT-5.4

Reasoning

MiniMax M2.7

Non-Reasoning

Weight access

GPT-5.4

Proprietary

MiniMax M2.7

Open Weight

License

GPT-5.4

Proprietary

MiniMax M2.7

Open Weight

Release date

GPT-5.4

2026-03-05

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-5.4 has the higher public score, 68.22 versus 47.85, and the 90% score intervals do not overlap.
Workload cost: Repository review: $0.17 vs $0.0186. Cache-heavy agent loop: $0.25 vs $0.078.
Context tradeoff: GPT-5.4 has the larger documented window (1.05M).

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, GPT-5.4 or MiniMax M2.7?

GPT-5.4 has the higher public score, 68.22 versus 47.85, 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-5.4 or MiniMax M2.7?

GPT-5.4 leads the public coding lane, 49.8 to 36, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, GPT-5.4 or MiniMax M2.7?

GPT-5.4 leads the public agentic tasks lane, 49.5 to 29.1, with Supported evidence for both models and non-overlapping 90% intervals.

Which costs less, GPT-5.4 or MiniMax M2.7?

For the stated presets, chat costs $0.01 on GPT-5.4 and $0.0009 on MiniMax M2.7; repository review costs $0.17 and $0.0186; the cache-heavy agent loop costs $0.25 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-5.4 or MiniMax M2.7?

GPT-5.4 has the larger documented context window: 1.05M, 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 evidence53 rows

Agentic

Terminal-Bench 2.0
GPT-5.475.1%
Source
MiniMax M2.757%
Source
GPT-5.4 leads this result
CyberGym
GPT-5.479.0%
Source
MiniMax M2.7—
Not directly comparable
BrowseComp
GPT-5.482.7%
Source
MiniMax M2.7—
Not directly comparable
OSWorld-Verified
GPT-5.475%
Source
MiniMax M2.7—
Not directly comparable
MCP Atlas
GPT-5.470.6%
Source
MiniMax M2.7—
Not directly comparable
Toolathlon
GPT-5.454.6%
Source
MiniMax M2.746.3%
Source
GPT-5.4 leads this result
τ²-bench results
GPT-5.498.9%
Source
MiniMax M2.7—
Not directly comparable
Claw-Eval
Shared source
GPT-5.460.3%
MiniMax M2.748.7%
GPT-5.4 leads this result
DeepSearchQA
GPT-5.473.6%
Source
MiniMax M2.7—
Not directly comparable
Gert Labs
Shared source
GPT-5.464.89%
MiniMax M2.740.40%
GPT-5.4 leads this result
ResearchClawBench
GPT-5.415.3%
Source
MiniMax M2.7—
Not directly comparable
JobBench
GPT-5.438.9%
Source
MiniMax M2.7—
Not directly comparable
ExploitGym
GPT-5.46.0%
Source
MiniMax M2.7—
Not directly comparable
ApprenticeBench
GPT-5.411%
Source
MiniMax M2.7—
Not directly comparable
MLE-Bench Lite
GPT-5.4—
MiniMax M2.766.6%
Source
Not directly comparable
MM-ClawBench
GPT-5.4—
MiniMax M2.762.7%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.4—
MiniMax M2.748.7%
Source
Not directly comparable

Coding

LiveCodeBench Pro
GPT-5.487.5%
Source
MiniMax M2.7—
Not directly comparable
SWE-bench Pro
GPT-5.457.7%
Source
MiniMax M2.756.2%
Source
GPT-5.4 leads this result
React Native Evals
Shared source
GPT-5.485.3%
MiniMax M2.771.4%
GPT-5.4 leads this result
Vibe Code Bench
Shared source
GPT-5.467.42%
MiniMax M2.727.04%
GPT-5.4 leads this result
SWE-bench Verified*
GPT-5.4—
MiniMax M2.775.4%
Source
Not directly comparable
SWE-Rebench
GPT-5.4—
MiniMax M2.751.9%
Source
Not directly comparable
SWE Multilingual
GPT-5.4—
MiniMax M2.776.5%
Source
Not directly comparable
Multi-SWE Bench
GPT-5.4—
MiniMax M2.752.7%
Source
Not directly comparable
VIBE-Pro
GPT-5.4—
MiniMax M2.755.6%
Source
Not directly comparable
NL2Repo
GPT-5.4—
MiniMax M2.739.8%
Source
Not directly comparable
LiveCodeBench (Vals)
GPT-5.4—
MiniMax M2.779.9%
Source
Not directly comparable
SWE-bench (Vals)
GPT-5.4—
MiniMax M2.773.8%
Source
Not directly comparable

Reasoning

ARC-AGI-2
GPT-5.474.0%
Source
MiniMax M2.7—
Not directly comparable
ARC-AGI-3
GPT-5.40.2%
Source
MiniMax M2.7—
Not directly comparable

Multimodal

MMMU-Pro
GPT-5.481.2%
Source
MiniMax M2.7—
Not directly comparable
OfficeQA Pro
GPT-5.453.2%
Source
MiniMax M2.7—
Not directly comparable
MMMU-Pro w/ Python
GPT-5.482.1%
Source
MiniMax M2.7—
Not directly comparable
CharXiv
GPT-5.482.8%
Source
MiniMax M2.7—
Not directly comparable
ERQA
GPT-5.465.4%
Source
MiniMax M2.7—
Not directly comparable
SimpleVQA
GPT-5.461.1%
Source
MiniMax M2.7—
Not directly comparable
ScreenSpot Pro
GPT-5.485.4%
Source
MiniMax M2.7—
Not directly comparable
ZeroBench
GPT-5.441.0%
Source
MiniMax M2.7—
Not directly comparable
MedXpertQA (MM)
GPT-5.477.1%
Source
MiniMax M2.7—
Not directly comparable

Knowledge

GPQA
GPT-5.492.8%
Source
MiniMax M2.7—
Not directly comparable
HLE
GPT-5.452.1%
Source
MiniMax M2.7—
Not directly comparable
HLE w/o tools
GPT-5.439.8%
Source
MiniMax M2.7—
Not directly comparable
GPQA-D
GPT-5.492.8%
Source
MiniMax M2.787.0%
Source
GPT-5.4 leads this result
HealthBench Hard
GPT-5.440.1%
Source
MiniMax M2.7—
Not directly comparable
MedXpertQA (Text)
GPT-5.459.6%
Source
MiniMax M2.7—
Not directly comparable
HealthBench Professional
GPT-5.448.1%
Source
MiniMax M2.7—
Not directly comparable
MMLU-Pro (Arcee)
GPT-5.4—
MiniMax M2.780.8%
Source
Not directly comparable
GPQA Diamond (Vals)
GPT-5.4—
MiniMax M2.786.6%
Source
Not directly comparable
MMLU-Pro (Vals)
GPT-5.4—
MiniMax M2.780.4%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
GPT-5.447.600%
Source
MiniMax M2.7—
Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.427.100%
Source
MiniMax M2.7—
Not directly comparable
AIME25 (Arcee)
GPT-5.4—
MiniMax M2.780.0%
Source
Not directly comparable

53 public results · 8 shared

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Last updated September 29, 2026

GPT-5.4 vs MiniMax M2.7

Decision reading

Which one for your work

Coding work

Agentic work

Long documents

Chat turn cost

Repository review cost

Cache-heavy agent loop cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Knowledge

Instruction following

Reasoning

Multimodal

Multilingual

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, GPT-5.4 or MiniMax M2.7?

Which is better for coding, GPT-5.4 or MiniMax M2.7?

Which is better for agentic tasks, GPT-5.4 or MiniMax M2.7?

Which costs less, GPT-5.4 or MiniMax M2.7?

Which has the larger context window, GPT-5.4 or MiniMax M2.7?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch GPT-5.4 vs MiniMax M2.7