Model comparison
GPT-5.5 Pro vs MiniMax M2.7
Head-to-head evidence from 1 shared benchmark result across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.5 Pro #38 (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 Pro and MiniMax M2.7 share 1 comparable benchmark result. 1 of 8 categories are comparable. 6 results are unique to GPT-5.5 Pro; 34 to MiniMax M2.7.
Updated July 22, 2026- Shared results
- 1
- GPT-5.5 Pro only
- 6
- MiniMax M2.7 only
- 34
- Comparable categories
- 1 / 8
Pick MiniMax M2.7 if you want the stronger benchmark profile. GPT-5.5 Pro only becomes the better choice if agentic is the priority or you need the larger 1M context window.
Confidence note. This is a partial-evidence comparison with 1 shared benchmark result across 1 evidence category; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
MiniMax M2.7 has the cleaner BenchAlign overall profile here, landing at 64.11 versus 63.69. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
GPT-5.5 Pro is also the more expensive model on tokens at $30.00 input / $180.00 output per 1M tokens, versus $0.30 input / $1.20 output per 1M tokens for MiniMax M2.7. That is roughly 150.0x on output cost alone. GPT-5.5 Pro 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 Pro 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 Pro | Δ | MiniMax M2.7 |
|---|---|---|---|
| Agentic | GPT-5.5 Pro90.1 | Margin← 33.1 | MiniMax M2.757.0 |
| Coding | GPT-5.5 ProNot measured | MarginNo overlap | MiniMax M2.753.3 |
| Knowledge | GPT-5.5 Pro57.2 | MarginNo overlap | MiniMax M2.7Not measured |
| Math | GPT-5.5 Pro48.1 | MarginNo overlap | MiniMax M2.7Not measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.5 Pro | MiniMax M2.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.5 Pro$30 input / $180 output | MiniMax M2.7$0.3 input / $1.2 output | MiniMax M2.7 has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.5 ProNot available | MiniMax M2.745 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.5 ProNot available | MiniMax M2.72.53 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.5 Pro1M | MiniMax M2.7200K | GPT-5.5 Pro lists the larger context window. |
Benchmark Deep Dive
AgenticGPT-5.5 Pro wins12 benchmarks
| Benchmark | GPT-5.5 Pro | MiniMax M2.7 | Result |
|---|---|---|---|
| BrowseCompSource | 90.1% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 57% | Not comparable |
| τ²-bench resultsSource | — | 84.8% | Not comparable |
| ToolathlonSource | — | 46.3% | Not comparable |
| MLE-Bench LiteSource | — | 66.6% | Not comparable |
| MM-ClawBenchSource | — | 62.7% | Not comparable |
| Claw-EvalSource | — | 48.7% | Not comparable |
| AA Agentic IndexSource | — | 25.6% | Not comparable |
| APEX-Agents-AASource | — | 10.6% | Not comparable |
| GDPval-AASource | — | 32.9% | Not comparable |
| GDPval-AASource | — | 1158 | Not comparable |
| Gert LabsSource | — | 40.40% | Not comparable |
Coding11 benchmarks
| Benchmark | GPT-5.5 Pro | MiniMax M2.7 | Result |
|---|---|---|---|
| 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 |
| Vibe Code BenchSource | — | 27.04% | Not comparable |
| React Native EvalsSource | — | 71.4% | Not comparable |
| AA Coding IndexSource | — | 52.6% | Not comparable |
| AA-SciCodeSource | — | 47.0% | Not comparable |
Reasoning2 benchmarks
Knowledge10 benchmarks
| Benchmark | GPT-5.5 Pro | MiniMax M2.7 | Result |
|---|---|---|---|
| HLESource | 57.2% | — | Not comparable |
| HLE w/o toolsSource | 43.1% | — | Not comparable |
| GPQA-DSource | — | 87.0% | Not comparable |
| MMLU-Pro (Arcee)Source | — | 80.8% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 38.1% | Not comparable |
| AA-GPQA DiamondSource | — | 87.4% | Not comparable |
| AA-HLESource | — | 28.1% | Not comparable |
| AA-Omniscience IndexSource | — | 0.7% | Not comparable |
| AA-Omniscience AccuracySource | — | 26.1% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 34.4% | Not comparable |
Math4 benchmarks
Multimodal1 benchmarks
| Benchmark | GPT-5.5 Pro | MiniMax M2.7 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | — | 1275 | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GPT-5.5 Pro | MiniMax M2.7 | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 75.7% | Not comparable |
Frequently Asked Questions (2)
Which is better, GPT-5.5 Pro or MiniMax M2.7?
MiniMax M2.7 is ahead on BenchLM's BenchAlign leaderboard, 64.11 to 63.69.
Which is better for agentic tasks, GPT-5.5 Pro or MiniMax M2.7?
GPT-5.5 Pro has the edge for agentic tasks in this comparison, averaging 90.1 versus 57. MiniMax M2.7 stays close enough that the answer can still flip depending on your workload.
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