Model comparison
GPT-5.6 Sol 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-5.6 Sol #3 (Supported); MiniMax M2.7 #36 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.6 Sol and MiniMax M2.7 share 19 comparable benchmark results. 2 of 8 categories are comparable. 27 results are unique to GPT-5.6 Sol; 16 to MiniMax M2.7.
Updated July 23, 2026- Shared results
- 19
- GPT-5.6 Sol only
- 27
- MiniMax M2.7 only
- 16
- Comparable categories
- 2 / 8
Pick GPT-5.6 Sol 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 19 shared benchmark results across 5 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-5.6 Sol is clearly ahead on the BenchAlign aggregate, 81.96 to 64.11. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.6 Sol's sharpest advantage is in agentic, where it averages 92 against 57. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 91.9% to 57%.
GPT-5.6 Sol 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-5.6 Sol 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-5.6 Sol 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-5.6 Sol | Δ | MiniMax M2.7 |
|---|---|---|---|
| Agentic | GPT-5.6 Sol92.0 | Margin← 35.0 | MiniMax M2.757.0 |
| Coding | GPT-5.6 Sol64.6 | Margin← 11.3 | MiniMax M2.753.3 |
| Knowledge | GPT-5.6 Sol94.6 | MarginNo overlap | MiniMax M2.7Not measured |
| Math | GPT-5.6 Sol87.5 | MarginNo overlap | MiniMax M2.7Not measured |
| Multimodal | GPT-5.6 Sol83.0 | 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 91.9%B 57%Winner: GPT-5.6 SolΔ 34.9Terminal-Bench 2.0: GPT-5.6 Sol scored 91.9%; MiniMax M2.7 scored 57%. GPT-5.6 Sol wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 64.6%B 56.2%Winner: GPT-5.6 SolΔ 8.4SWE-bench Pro: GPT-5.6 Sol scored 64.6%; MiniMax M2.7 scored 56.2%. GPT-5.6 Sol wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.6 Sol | MiniMax M2.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.6 Sol$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-5.6 SolNot available | MiniMax M2.745 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.6 SolNot available | MiniMax M2.72.53 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.6 Sol1M | MiniMax M2.7200K | GPT-5.6 Sol lists the larger context window. |
Benchmark Deep Dive
AgenticGPT-5.6 Sol wins22 benchmarks
| Benchmark | GPT-5.6 Sol | MiniMax M2.7 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 91.9% | 57% | GPT-5.6 Sol leads |
| BrowseCompSource | 92.2% | — | Not comparable |
| OSWorld 2.0Source | 62.6% | — | Not comparable |
| CyberGymSource | 84.5% | — | Not comparable |
| ExploitGymSource | 33.7% | — | Not comparable |
| ToolathlonSource | 58% | 46.3% | GPT-5.6 Sol leads |
| AA Agentic IndexSource | 54.0% | 25.6% | GPT-5.6 Sol leads |
| τ²-bench resultsSource | 85.1% | 84.8% | GPT-5.6 Sol leads |
| GDPval-AASource | 61.8% | 32.9% | GPT-5.6 Sol leads |
| GDPval-AASource | 1736 | 1158 | GPT-5.6 Sol leads |
| AA BriefcaseSource | 1501 | — | Not comparable |
| AA ITBenchSource | 56.2% | — | Not comparable |
| AA Tau3 BankingSource | 33.0% | — | Not comparable |
| AA AutomationBenchSource | 51.2% | — | Not comparable |
| AA Harvey LABSource | 87.2% | — | Not comparable |
| terminalBenchHardSource | 65.9% | — | Not comparable |
| aaTerminalBench21Source | 88% | — | Not comparable |
| MLE-Bench LiteSource | — | 66.6% | Not comparable |
| MM-ClawBenchSource | — | 62.7% | Not comparable |
| Claw-EvalSource | — | 48.7% | Not comparable |
| APEX-Agents-AASource | — | 10.6% | Not comparable |
| Gert LabsSource | — | 40.40% | Not comparable |
CodingGPT-5.6 Sol wins16 benchmarks
| Benchmark | GPT-5.6 Sol | MiniMax M2.7 | Result |
|---|---|---|---|
| SWE-bench ProSource | 64.6% | 56.2% | GPT-5.6 Sol leads |
| Terminal-Bench 2.0Source | 91.9% | — | Not comparable |
| deepSweSource | 72.7% | — | Not comparable |
| FrontierCode 1.1 ExtendedSource | 60.6% | — | Not comparable |
| cursorBench32Source | 67.2% | — | Not comparable |
| VulcanBench v3Source | 87.0% | — | Not comparable |
| AA Coding IndexSource | 77.4% | 52.6% | GPT-5.6 Sol leads |
| AA-SciCodeSource | 56.1% | 47.0% | GPT-5.6 Sol 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 |
| Vibe Code BenchSource | — | 27.04% | Not comparable |
| React Native EvalsSource | — | 71.4% | Not comparable |
Reasoning4 benchmarks
Knowledge11 benchmarks
| Benchmark | GPT-5.6 Sol | MiniMax M2.7 | Result |
|---|---|---|---|
| GPQASource | 94.6% | — | Not comparable |
| GPQA-DSource | 94.6% | 87.0% | GPT-5.6 Sol leads |
| HealthBench ProfessionalSource | 60.5% | — | Not comparable |
| HealthBench HardSource | 33.1% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 58.9% | 38.1% | GPT-5.6 Sol leads |
| AA-GPQA DiamondSource | 94.1% | 87.4% | GPT-5.6 Sol leads |
| AA-HLESource | 47.2% | 28.1% | GPT-5.6 Sol leads |
| AA-Omniscience IndexSource | 21.7% | 0.7% | GPT-5.6 Sol leads |
| AA-Omniscience AccuracySource | 58.5% | 26.1% | GPT-5.6 Sol leads |
| AA-Omniscience Hallucination RateSource | 88.8% | 34.4% | MiniMax M2.7 leads |
| MMLU-Pro (Arcee)Source | — | 80.8% | Not comparable |
Math4 benchmarks
Multimodal4 benchmarks
Inst. Following1 benchmarks
| Benchmark | GPT-5.6 Sol | MiniMax M2.7 | Result |
|---|---|---|---|
| AA-IFBenchSource | 72.7% | 75.7% | MiniMax M2.7 leads |
Frequently Asked Questions (3)
Which is better, GPT-5.6 Sol or MiniMax M2.7?
GPT-5.6 Sol is ahead on BenchLM's BenchAlign leaderboard, 81.96 to 64.11. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 91.9% and 57%.
Which is better for coding, GPT-5.6 Sol or MiniMax M2.7?
GPT-5.6 Sol has the edge for coding in this comparison, averaging 64.6 versus 53.3. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GPT-5.6 Sol or MiniMax M2.7?
GPT-5.6 Sol has the edge for agentic tasks in this comparison, averaging 92 versus 57. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
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