Model comparison
GPT-5.1-Codex vs MiniMax M2.7
Head-to-head evidence from 14 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.1-Codex #99 (Estimated); MiniMax M2.7 #36 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.1-Codex and MiniMax M2.7 share 14 comparable benchmark results. 0 of 8 categories are comparable. 2 results are unique to GPT-5.1-Codex; 21 to MiniMax M2.7.
Updated July 23, 2026- Shared results
- 14
- GPT-5.1-Codex only
- 2
- MiniMax M2.7 only
- 21
- Comparable categories
- 0 / 8
Benchmark data for GPT-5.1-Codex and MiniMax M2.7 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 14 shared benchmark results across 6 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
GPT-5.1-Codex has 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-5.1-Codex | Δ | MiniMax M2.7 |
|---|---|---|---|
| Agentic | GPT-5.1-CodexNot measured | MarginNo overlap | MiniMax M2.757.0 |
| Coding | GPT-5.1-CodexNot measured | MarginNo overlap | MiniMax M2.753.3 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.1-Codex | MiniMax M2.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.1-CodexNot available | MiniMax M2.7$0.3 input / $1.2 output | A complete price comparison is not available. |
| Generation speedtokens per second | GPT-5.1-CodexNot available | MiniMax M2.745 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.1-CodexNot available | MiniMax M2.72.53 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.1-Codex400K | MiniMax M2.7200K | GPT-5.1-Codex lists the larger context window. |
Benchmark Deep Dive
Agentic12 benchmarks
| Benchmark | GPT-5.1-Codex | MiniMax M2.7 | Result |
|---|---|---|---|
| τ²-bench resultsSource | 83% | 84.8% | MiniMax M2.7 leads |
| Gert LabsSource | 49.68% | 40.40% | GPT-5.1-Codex leads |
| JobBenchSource | 26.2% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 57% | 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 |
Coding11 benchmarks
| Benchmark | GPT-5.1-Codex | MiniMax M2.7 | Result |
|---|---|---|---|
| Vibe Code BenchSource | 13.12% | 27.04% | MiniMax M2.7 leads |
| AA-SciCodeSource | 40.2% | 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 |
| AA Coding IndexSource | — | 52.6% | Not comparable |
Reasoning2 benchmarks
Knowledge8 benchmarks
| Benchmark | GPT-5.1-Codex | MiniMax M2.7 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 34.7% | 38.1% | MiniMax M2.7 leads |
| AA-GPQA DiamondSource | 86.0% | 87.4% | MiniMax M2.7 leads |
| AA-HLESource | 23.4% | 28.1% | MiniMax M2.7 leads |
| AA-Omniscience IndexSource | -6.0% | 0.7% | MiniMax M2.7 leads |
| AA-Omniscience AccuracySource | 39.2% | 26.1% | GPT-5.1-Codex leads |
| AA-Omniscience Hallucination RateSource | 74.4% | 34.4% | MiniMax M2.7 leads |
| GPQA-DSource | — | 87.0% | Not comparable |
| MMLU-Pro (Arcee)Source | — | 80.8% | Not comparable |
Math1 benchmarks
| Benchmark | GPT-5.1-Codex | MiniMax M2.7 | Result |
|---|---|---|---|
| AIME25 (Arcee)Source | — | 80.0% | Not comparable |
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | GPT-5.1-Codex | MiniMax M2.7 | Result |
|---|---|---|---|
| AA-IFBenchSource | 70.0% | 75.7% | MiniMax M2.7 leads |
Frequently Asked Questions (3)
Can I compare GPT-5.1-Codex and MiniMax M2.7 on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for GPT-5.1-Codex and MiniMax M2.7 today?
MiniMax M2.7: $0.30 input / $1.20 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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