Model comparison
GPT-5.4 vs MiniMax M2.7
Head-to-head evidence from 25 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.4 #8 (Supported); MiniMax M2.7 #36 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.4 and MiniMax M2.7 share 25 comparable benchmark results. 2 of 8 categories are comparable. 27 results are unique to GPT-5.4; 10 to MiniMax M2.7.
Updated July 23, 2026- Shared results
- 25
- GPT-5.4 only
- 27
- MiniMax M2.7 only
- 10
- Comparable categories
- 2 / 8
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.
Confidence note. This is a partial-evidence comparison with 25 shared benchmark results across 6 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GPT-5.4 is clearly ahead on the BenchAlign aggregate, 74.24 to 64.11. 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 77.2 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.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | GPT-5.4 | Δ | MiniMax M2.7 |
|---|---|---|---|
| Agentic | GPT-5.477.2 | Margin← 20.2 | MiniMax M2.757.0 |
| Coding | GPT-5.457.7 | Margin← 4.4 | MiniMax M2.753.3 |
| Knowledge | GPT-5.457.6 | MarginNo overlap | MiniMax M2.7Not measured |
| Math | GPT-5.442.5 | MarginNo overlap | MiniMax M2.7Not measured |
| Multimodal | GPT-5.473.2 | MarginNo overlap | MiniMax M2.7Not measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 75.1%B 57%Winner: GPT-5.4Δ 18.1Terminal-Bench 2.0: GPT-5.4 scored 75.1%; MiniMax M2.7 scored 57%. GPT-5.4 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 57.7%B 56.2%Winner: GPT-5.4Δ 1.5SWE-bench Pro: GPT-5.4 scored 57.7%; MiniMax M2.7 scored 56.2%. GPT-5.4 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.4 | MiniMax M2.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.4$2.5 input / $15 output | MiniMax M2.7$0.3 input / $1.2 output | MiniMax M2.7 has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.474 tok/s | MiniMax M2.745 tok/s | GPT-5.4 has the higher measured throughput. |
| First-answer latencyseconds to first token | GPT-5.4151.79 s | MiniMax M2.72.53 s | MiniMax M2.7 reaches the first token sooner. |
| Context windowmaximum listed tokens | GPT-5.41.05M | MiniMax M2.7200K | GPT-5.4 lists the larger context window. |
Benchmark Deep Dive
AgenticGPT-5.4 wins19 benchmarks
| Benchmark | GPT-5.4 | MiniMax M2.7 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 75.1% | 57% | GPT-5.4 leads |
| CyberGymSource | 79.0% | — | Not comparable |
| BrowseCompSource | 82.7% | — | Not comparable |
| OSWorld-VerifiedSource | 75% | — | Not comparable |
| MCP AtlasSource | 70.6% | — | Not comparable |
| ToolathlonSource | 54.6% | 46.3% | GPT-5.4 leads |
| τ²-bench resultsSource | 87.1% | 84.8% | GPT-5.4 leads |
| Claw-EvalSource | 60.3% | 48.7% | GPT-5.4 leads |
| DeepSearchQASource | 73.6% | — | Not comparable |
| AA Agentic IndexSource | 41.1% | 25.6% | GPT-5.4 leads |
| APEX-Agents-AASource | 33.3% | 10.6% | GPT-5.4 leads |
| GDPval-AASource | 44.7% | 32.9% | GPT-5.4 leads |
| GDPval-AASource | 1395 | 1158 | GPT-5.4 leads |
| Gert LabsSource | 64.89% | 40.40% | GPT-5.4 leads |
| ResearchClawBenchSource | 15.3% | — | Not comparable |
| JobBenchSource | 38.9% | — | Not comparable |
| ExploitGymSource | 6.0% | — | Not comparable |
| MLE-Bench LiteSource | — | 66.6% | Not comparable |
| MM-ClawBenchSource | — | 62.7% | Not comparable |
CodingGPT-5.4 wins12 benchmarks
| Benchmark | GPT-5.4 | MiniMax M2.7 | Result |
|---|---|---|---|
| LiveCodeBench ProSource | 87.5% | — | Not comparable |
| SWE-bench ProSource | 57.7% | 56.2% | GPT-5.4 leads |
| React Native EvalsSource | 85.3% | 71.4% | GPT-5.4 leads |
| Vibe Code BenchSource | 67.42% | 27.04% | GPT-5.4 leads |
| AA Coding IndexSource | 71.0% | 52.6% | GPT-5.4 leads |
| AA-SciCodeSource | 56.6% | 47.0% | GPT-5.4 leads |
| SWE-bench Verified*Source | — | 75.4% | Not comparable |
| SWE-RebenchSource | — | 51.9% | Not comparable |
| SWE MultilingualSource | — | 76.5% | Not comparable |
| Multi-SWE BenchSource | — | 52.7% | Not comparable |
| VIBE-ProSource | — | 55.6% | Not comparable |
| NL2RepoSource | — | 39.8% | Not comparable |
Reasoning2 benchmarks
Knowledge14 benchmarks
| Benchmark | GPT-5.4 | MiniMax M2.7 | Result |
|---|---|---|---|
| GPQASource | 92.8% | — | Not comparable |
| HLESource | 52.1% | — | Not comparable |
| HLE w/o toolsSource | 39.8% | — | Not comparable |
| GPQA-DSource | 92.8% | 87.0% | GPT-5.4 leads |
| HealthBench HardSource | 40.1% | — | Not comparable |
| MedXpertQA (Text)Source | 59.6% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 51.4% | 38.1% | GPT-5.4 leads |
| AA-GPQA DiamondSource | 92.0% | 87.4% | GPT-5.4 leads |
| AA-HLESource | 41.6% | 28.1% | GPT-5.4 leads |
| AA-Omniscience IndexSource | 5.7% | 0.7% | GPT-5.4 leads |
| AA-Omniscience AccuracySource | 50.0% | 26.1% | GPT-5.4 leads |
| AA-Omniscience Hallucination RateSource | 88.6% | 34.4% | MiniMax M2.7 leads |
| HealthBench ProfessionalSource | 48.1% | — | Not comparable |
| MMLU-Pro (Arcee)Source | — | 80.8% | Not comparable |
Math3 benchmarks
Multimodal11 benchmarks
| Benchmark | GPT-5.4 | MiniMax M2.7 | Result |
|---|---|---|---|
| MMMU-ProSource | 81.2% | — | Not comparable |
| OfficeQA ProSource | 53.2% | — | Not comparable |
| MMMU-Pro w/ PythonSource | 82.1% | — | Not comparable |
| CharXivSource | 82.8% | — | Not comparable |
| ERQASource | 65.4% | — | Not comparable |
| SimpleVQASource | 61.1% | — | Not comparable |
| ScreenSpot ProSource | 85.4% | — | Not comparable |
| ZeroBenchSource | 41.0% | — | Not comparable |
| MedXpertQA (MM)Source | 77.1% | — | Not comparable |
| AA-MMMU-ProSource | 78.4% | — | Not comparable |
| Design Arena WebsiteSource | 1250 | 1275 | MiniMax M2.7 leads |
Inst. Following1 benchmarks
| Benchmark | GPT-5.4 | MiniMax M2.7 | Result |
|---|---|---|---|
| AA-IFBenchSource | 73.9% | 75.7% | MiniMax M2.7 leads |
Frequently Asked Questions (3)
Which is better, GPT-5.4 or MiniMax M2.7?
GPT-5.4 is ahead on BenchLM's BenchAlign leaderboard, 74.24 to 64.11. 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 57.7 versus 53.3. Inside this category, Vibe Code Bench 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 77.2 versus 57. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
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