GPT-5.4 vs MiniMax M2.7

Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.

GPT-5.4 is clearly ahead on the aggregate, 80 to 57. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

GPT-5.4's sharpest advantage is in agentic, where it averages 77 against 57. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 75.1% to 57%.

GPT-5.4 is also the more expensive model on tokens at $2.50 input / $15.00 output per 1M tokens, versus $0.30 input / $1.20 output per 1M tokens for MiniMax M2.7. That is roughly 12.5x on output cost alone. GPT-5.4 is the reasoning model in the pair, while MiniMax M2.7 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. GPT-5.4 gives you the larger context window at 1.05M, compared with 200K for MiniMax M2.7.

Quick Verdict

Pick GPT-5.4 if you want the stronger benchmark profile. MiniMax M2.7 only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.

Agentic

GPT-5.4

GPT-5.4

77

MiniMax M2.7

57

75.1%
Terminal-Bench 2.0
57%
82.7%
BrowseComp
Coming soon
75%
OSWorld-Verified
Coming soon
67.2%
MCP Atlas
Coming soon
54.6%
Toolathlon
46.3%
98.9%
tau2-bench
Coming soon
Coming soon
MLE-Bench Lite
66.6%
Coming soon
MM-ClawBench
62.7%

Coding

GPT-5.4

GPT-5.4

72.8

MiniMax M2.7

56.2

95%
HumanEval
Coming soon
84%
SWE-bench Verified
Coming soon
84%
LiveCodeBench
Coming soon
57.7%
SWE-bench Pro
56.2%
Coming soon
SWE Multilingual
76.5%
Coming soon
Multi-SWE Bench
52.7%
Coming soon
VIBE-Pro
55.6%
Coming soon
NL2Repo
39.8%

Multimodal & Grounded

Coming soon

Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.

81.2%
MMMU-Pro
Coming soon
96%
OfficeQA Pro
Coming soon
81.5%
MMMU-Pro w/ Python
Coming soon
0.1090
OmniDocBench 1.5
Coming soon
Coming soon
GDPval-AA
1495

Reasoning

Coming soon

Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.

94%
MuSR
Coming soon
97%
BBH
Coming soon
95%
LongBench v2
Coming soon
97%
MRCRv2
Coming soon
86%
MRCR v2 64K-128K
Coming soon
79.3%
MRCR v2 128K-256K
Coming soon
93.1%
Graphwalks BFS 128K
Coming soon
89.8%
Graphwalks Parents 128K
Coming soon
73.3%
ARC-AGI-2
Coming soon

Knowledge

Coming soon

Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.

92.8%
GPQA
Coming soon
96%
SuperGPQA
Coming soon
93%
MMLU-Pro
Coming soon
48%
HLE
Coming soon
91%
FrontierScience
Coming soon
39.8%
HLE w/o tools
Coming soon
97%
SimpleQA
Coming soon
Coming soon
Artificial Analysis
50

Instruction Following

Coming soon

Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.

96%
IFEval
Coming soon

Multilingual

Coming soon

Comparable scores for this category are coming soon. One or both models do not have sourced results here yet.

94%
MMLU-ProX
Coming soon

Mathematics

Coming soon

Benchmark data for this category is coming soon.

Frequently Asked Questions

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

GPT-5.4 is ahead overall, 80 to 57. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 75.1% and 57%.

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

GPT-5.4 has the edge for coding in this comparison, averaging 72.8 versus 56.2. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.

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

GPT-5.4 has the edge for agentic tasks in this comparison, averaging 77 versus 57. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.

Last updated: March 18, 2026

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