Model comparison
GPT-5.4 nano vs MiniMax M2.7
Head-to-head evidence from 19 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.4 nano #25 (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 nano and MiniMax M2.7 share 19 comparable benchmark results. 1 of 8 categories are comparable. 10 results are unique to GPT-5.4 nano; 16 to MiniMax M2.7.
Updated July 23, 2026- Shared results
- 19
- GPT-5.4 nano only
- 10
- MiniMax M2.7 only
- 16
- Comparable categories
- 1 / 8
Pick GPT-5.4 nano if you want the stronger benchmark profile. MiniMax M2.7 only becomes the better choice if agentic is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 19 shared benchmark results across 5 evidence categories; 1 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 nano has the cleaner BenchAlign overall profile here, landing at 66.79 versus 64.11. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
GPT-5.4 nano is also the more expensive model on tokens at $0.20 input / $1.25 output per 1M tokens, versus $0.30 input / $1.20 output per 1M tokens for MiniMax M2.7. GPT-5.4 nano 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 nano gives you the larger context window at 400K, 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 nano | Δ | MiniMax M2.7 |
|---|---|---|---|
| Agentic | GPT-5.4 nano42.9 | Margin→ 14.1 | MiniMax M2.757.0 |
| Coding | GPT-5.4 nanoNot measured | MarginNo overlap | MiniMax M2.753.3 |
| Knowledge | GPT-5.4 nano43.8 | MarginNo overlap | MiniMax M2.7Not measured |
| Math | GPT-5.4 nano21.0 | MarginNo overlap | MiniMax M2.7Not measured |
| Multimodal | GPT-5.4 nano66.1 | 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 46.3%B 57%Winner: MiniMax M2.7Δ 10.7Terminal-Bench 2.0: GPT-5.4 nano scored 46.3%; MiniMax M2.7 scored 57%. MiniMax M2.7 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.4 nano | MiniMax M2.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.4 nano$0.2 input / $1.25 output | MiniMax M2.7$0.3 input / $1.2 output | GPT-5.4 nano has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.4 nano191 tok/s | MiniMax M2.745 tok/s | GPT-5.4 nano has the higher measured throughput. |
| First-answer latencyseconds to first token | GPT-5.4 nano3.64 s | MiniMax M2.72.53 s | MiniMax M2.7 reaches the first token sooner. |
| Context windowmaximum listed tokens | GPT-5.4 nano400K | MiniMax M2.7200K | GPT-5.4 nano lists the larger context window. |
Benchmark Deep Dive
AgenticMiniMax M2.7 wins13 benchmarks
| Benchmark | GPT-5.4 nano | MiniMax M2.7 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 46.3% | 57% | MiniMax M2.7 leads |
| OSWorld-VerifiedSource | 39% | — | Not comparable |
| MCP AtlasSource | 56.1% | — | Not comparable |
| ToolathlonSource | 35.5% | 46.3% | MiniMax M2.7 leads |
| τ²-bench resultsSource | 76% | 84.8% | MiniMax M2.7 leads |
| AA Agentic IndexSource | 27.5% | 25.6% | GPT-5.4 nano leads |
| APEX-Agents-AASource | 24.9% | 10.6% | GPT-5.4 nano leads |
| GDPval-AASource | 30.0% | 32.9% | MiniMax M2.7 leads |
| GDPval-AASource | 1100 | 1158 | MiniMax M2.7 leads |
| MLE-Bench LiteSource | — | 66.6% | Not comparable |
| MM-ClawBenchSource | — | 62.7% | Not comparable |
| Claw-EvalSource | — | 48.7% | Not comparable |
| Gert LabsSource | — | 40.40% | Not comparable |
Coding11 benchmarks
| Benchmark | GPT-5.4 nano | MiniMax M2.7 | Result |
|---|---|---|---|
| Vibe Code BenchSource | 26.10% | 27.04% | MiniMax M2.7 leads |
| AA Coding IndexSource | 56.1% | 52.6% | GPT-5.4 nano leads |
| AA-SciCodeSource | 46.9% | 47.0% | MiniMax M2.7 leads |
| SWE-bench Verified*Source | — | 75.4% | Not comparable |
| SWE-bench ProSource | — | 56.2% | 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 |
| React Native EvalsSource | — | 71.4% | Not comparable |
Reasoning2 benchmarks
Knowledge11 benchmarks
| Benchmark | GPT-5.4 nano | MiniMax M2.7 | Result |
|---|---|---|---|
| GPQASource | 82.8% | — | Not comparable |
| HLESource | 37.7% | — | Not comparable |
| HLE w/o toolsSource | 24.3% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 38.2% | 38.1% | GPT-5.4 nano leads |
| AA-GPQA DiamondSource | 81.7% | 87.4% | MiniMax M2.7 leads |
| AA-HLESource | 26.5% | 28.1% | MiniMax M2.7 leads |
| AA-Omniscience IndexSource | -29.5% | 0.7% | MiniMax M2.7 leads |
| AA-Omniscience AccuracySource | 25.4% | 26.1% | MiniMax M2.7 leads |
| AA-Omniscience Hallucination RateSource | 73.6% | 34.4% | MiniMax M2.7 leads |
| GPQA-DSource | — | 87.0% | Not comparable |
| MMLU-Pro (Arcee)Source | — | 80.8% | Not comparable |
Math3 benchmarks
Multimodal4 benchmarks
Inst. Following1 benchmarks
| Benchmark | GPT-5.4 nano | MiniMax M2.7 | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.9% | 75.7% | GPT-5.4 nano leads |
Frequently Asked Questions (2)
Which is better, GPT-5.4 nano or MiniMax M2.7?
GPT-5.4 nano is ahead on BenchLM's BenchAlign leaderboard, 66.79 to 64.11. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 46.3% and 57%.
Which is better for agentic tasks, GPT-5.4 nano or MiniMax M2.7?
MiniMax M2.7 has the edge for agentic tasks in this comparison, averaging 57 versus 42.9. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
Related Comparisons
Explore More
Choose a model with this week’s evidence
Join 2,000+ readers for ranking moves, pricing changes, and the claims that still need proof.
One email each week. Unsubscribe anytime.