Model comparison
GPT-5.5 vs MiniMax M2.7
Head-to-head evidence from 24 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.5 #9 (Estimated); MiniMax M2.7 #36 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.5 and MiniMax M2.7 share 24 comparable benchmark results. 2 of 8 categories are comparable. 33 results are unique to GPT-5.5; 11 to MiniMax M2.7.
Updated July 22, 2026- Shared results
- 24
- GPT-5.5 only
- 33
- MiniMax M2.7 only
- 11
- Comparable categories
- 2 / 8
Pick GPT-5.5 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 24 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-5.5 is clearly ahead on the BenchAlign aggregate, 73.51 to 64.11. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.5's sharpest advantage is in agentic, where it averages 81.6 against 57. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 82% to 57%.
GPT-5.5 is also the more expensive model on tokens at $5.00 input / $30.00 output per 1M tokens, versus $0.30 input / $1.20 output per 1M tokens for MiniMax M2.7. That is roughly 25.0x on output cost alone. GPT-5.5 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-5.5 gives you the larger context window at 1M, 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-5.5 | Δ | MiniMax M2.7 |
|---|---|---|---|
| Agentic | GPT-5.581.6 | Margin← 24.6 | MiniMax M2.757.0 |
| Coding | GPT-5.558.6 | Margin← 5.3 | MiniMax M2.753.3 |
| Reasoning | GPT-5.585.0 | MarginNo overlap | MiniMax M2.7Not measured |
| Knowledge | GPT-5.557.8 | MarginNo overlap | MiniMax M2.7Not measured |
| Math | GPT-5.547.6 | MarginNo overlap | MiniMax M2.7Not measured |
| Multimodal | GPT-5.570.4 | 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 82%B 57%Winner: GPT-5.5Δ 25Terminal-Bench 2.0: GPT-5.5 scored 82%; MiniMax M2.7 scored 57%. GPT-5.5 wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 58.6%B 56.2%Winner: GPT-5.5Δ 2.4SWE-bench Pro: GPT-5.5 scored 58.6%; MiniMax M2.7 scored 56.2%. GPT-5.5 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.5 | MiniMax M2.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.5$5 input / $30 output | MiniMax M2.7$0.3 input / $1.2 output | MiniMax M2.7 has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.5Not available | MiniMax M2.745 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.5Not available | MiniMax M2.72.53 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.51M | MiniMax M2.7200K | GPT-5.5 lists the larger context window. |
Benchmark Deep Dive
AgenticGPT-5.5 wins27 benchmarks
| Benchmark | GPT-5.5 | MiniMax M2.7 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 82% | 57% | GPT-5.5 leads |
| CyberGymSource | 81.8% | — | Not comparable |
| BrowseCompSource | 84.4% | — | Not comparable |
| OSWorld-VerifiedSource | 78.7% | — | Not comparable |
| MCP AtlasSource | 75.3% | — | Not comparable |
| ToolathlonSource | 55.6% | 46.3% | GPT-5.5 leads |
| τ²-bench resultsSource | 93.9% | 84.8% | GPT-5.5 leads |
| AA Agentic IndexSource | 44.9% | 25.6% | GPT-5.5 leads |
| APEX-Agents-AASource | 37.7% | 10.6% | GPT-5.5 leads |
| GDPval-AASource | 49.5% | 32.9% | GPT-5.5 leads |
| GDPval-AASource | 1490 | 1158 | GPT-5.5 leads |
| Gert LabsSource | 72.93% | 40.40% | GPT-5.5 leads |
| ResearchClawBenchSource | 17.0% | — | Not comparable |
| OSWorld 2.0Source | 13.0% | — | Not comparable |
| JobBenchSource | 42.7% | — | Not comparable |
| ExploitGymSource | 13.4% | — | Not comparable |
| AA BriefcaseSource | 1154 | — | Not comparable |
| AA AutomationBenchSource | 42.1% | — | Not comparable |
| AA EnterpriseOps-GymSource | 46.6% | — | Not comparable |
| AA Harvey LABSource | 86.3% | — | Not comparable |
| AA ITBenchSource | 45.8% | — | Not comparable |
| AA Tau3 BankingSource | 31.3% | — | Not comparable |
| terminalBenchHardSource | 60.6% | — | Not comparable |
| aaTerminalBench21Source | 84.3% | — | Not comparable |
| MLE-Bench LiteSource | — | 66.6% | Not comparable |
| MM-ClawBenchSource | — | 62.7% | Not comparable |
| Claw-EvalSource | — | 48.7% | Not comparable |
CodingGPT-5.5 wins15 benchmarks
| Benchmark | GPT-5.5 | MiniMax M2.7 | Result |
|---|---|---|---|
| SWE-bench ProSource | 58.6% | 56.2% | GPT-5.5 leads |
| Terminal-Bench 2.0Source | 82.0% | — | Not comparable |
| Vibe Code BenchSource | 69.85% | 27.04% | GPT-5.5 leads |
| React Native EvalsSource | 84.7% | 71.4% | GPT-5.5 leads |
| cursorBench31Source | 59.2% | — | Not comparable |
| cursorBench32Source | 58.4% | — | Not comparable |
| AA Coding IndexSource | 74.9% | 52.6% | GPT-5.5 leads |
| AA-SciCodeSource | 56.1% | 47.0% | GPT-5.5 leads |
| FrontierCode 1.1 MainSource | 43.0% | — | Not comparable |
| 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 |
Reasoning5 benchmarks
Knowledge11 benchmarks
| Benchmark | GPT-5.5 | MiniMax M2.7 | Result |
|---|---|---|---|
| GPQASource | 93.6% | — | Not comparable |
| GPQA-DSource | 93.6% | 87.0% | GPT-5.5 leads |
| HLESource | 52.2% | — | Not comparable |
| HLE w/o toolsSource | 41.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 54.8% | 38.1% | GPT-5.5 leads |
| AA-GPQA DiamondSource | 93.5% | 87.4% | GPT-5.5 leads |
| AA-HLESource | 44.3% | 28.1% | GPT-5.5 leads |
| AA-Omniscience IndexSource | 20.1% | 0.7% | GPT-5.5 leads |
| AA-Omniscience AccuracySource | 56.9% | 26.1% | GPT-5.5 leads |
| AA-Omniscience Hallucination RateSource | 85.5% | 34.4% | MiniMax M2.7 leads |
| MMLU-Pro (Arcee)Source | — | 80.8% | Not comparable |
Math4 benchmarks
Multimodal5 benchmarks
Inst. Following1 benchmarks
| Benchmark | GPT-5.5 | MiniMax M2.7 | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.9% | 75.7% | GPT-5.5 leads |
Frequently Asked Questions (3)
Which is better, GPT-5.5 or MiniMax M2.7?
GPT-5.5 is ahead on BenchLM's BenchAlign leaderboard, 73.51 to 64.11. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 82% and 57%.
Which is better for coding, GPT-5.5 or MiniMax M2.7?
GPT-5.5 has the edge for coding in this comparison, averaging 58.6 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-5.5 or MiniMax M2.7?
GPT-5.5 has the edge for agentic tasks in this comparison, averaging 81.6 versus 57. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
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