Model comparison
GPT-4o mini vs MiniMax M2.7
Head-to-head evidence from 9 shared benchmark results across 4 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-4o mini #183 (Supported); MiniMax M2.7 #36 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-4o mini and MiniMax M2.7 share 9 comparable benchmark results. 0 of 8 categories are comparable. 1 result is unique to GPT-4o mini; 26 to MiniMax M2.7.
Updated July 23, 2026- Shared results
- 9
- GPT-4o mini only
- 1
- MiniMax M2.7 only
- 26
- Comparable categories
- 0 / 8
Benchmark data for GPT-4o mini and MiniMax M2.7 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 9 shared benchmark results across 4 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.
MiniMax M2.7 is priced at $0.30 input / $1.20 output per 1M tokens, versus $0.15 input / $0.60 output per 1M tokens for GPT-4o mini. MiniMax M2.7 has the larger context window at 200K, compared with 128K for GPT-4o mini.
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-4o mini | Δ | MiniMax M2.7 |
|---|---|---|---|
| Agentic | GPT-4o miniNot measured | MarginNo overlap | MiniMax M2.757.0 |
| Coding | GPT-4o miniNot 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-4o mini | MiniMax M2.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-4o mini$0.15 input / $0.6 output | MiniMax M2.7$0.3 input / $1.2 output | GPT-4o mini has the lower combined listed price. |
| Generation speedtokens per second | GPT-4o mini33 tok/s | MiniMax M2.745 tok/s | MiniMax M2.7 has the higher measured throughput. |
| First-answer latencyseconds to first token | GPT-4o mini3.16 s | MiniMax M2.72.53 s | MiniMax M2.7 reaches the first token sooner. |
| Context windowmaximum listed tokens | GPT-4o mini128K | MiniMax M2.7200K | MiniMax M2.7 lists the larger context window. |
Benchmark Deep Dive
Agentic11 benchmarks
| Benchmark | GPT-4o mini | MiniMax M2.7 | Result |
|---|---|---|---|
| AA Agentic IndexSource | 1.0% | 25.6% | MiniMax M2.7 leads |
| GDPval-AASource | 0.0% | 32.9% | MiniMax M2.7 leads |
| GDPval-AASource | 226 | 1158 | MiniMax M2.7 leads |
| 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 |
| APEX-Agents-AASource | — | 10.6% | Not comparable |
| Gert LabsSource | — | 40.40% | Not comparable |
Coding11 benchmarks
| Benchmark | GPT-4o mini | MiniMax M2.7 | Result |
|---|---|---|---|
| AA-SciCodeSource | 22.9% | 47.0% | MiniMax M2.7 leads |
| AA Coding IndexSource | 11.4% | 52.6% | 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 |
| Vibe Code BenchSource | — | 27.04% | Not comparable |
| React Native EvalsSource | — | 71.4% | Not comparable |
Reasoning2 benchmarks
Knowledge8 benchmarks
| Benchmark | GPT-4o mini | MiniMax M2.7 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 6.9% | 38.1% | MiniMax M2.7 leads |
| AA-GPQA DiamondSource | 42.6% | 87.4% | MiniMax M2.7 leads |
| AA-HLESource | 4.0% | 28.1% | MiniMax M2.7 leads |
| GPQA-DSource | — | 87.0% | Not comparable |
| MMLU-Pro (Arcee)Source | — | 80.8% | Not comparable |
| AA-Omniscience IndexSource | — | 0.7% | Not comparable |
| AA-Omniscience AccuracySource | — | 26.1% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 34.4% | Not comparable |
Math1 benchmarks
| Benchmark | GPT-4o mini | MiniMax M2.7 | Result |
|---|---|---|---|
| AIME25 (Arcee)Source | — | 80.0% | Not comparable |
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | GPT-4o mini | MiniMax M2.7 | Result |
|---|---|---|---|
| AA-IFBenchSource | 31.0% | 75.7% | MiniMax M2.7 leads |
Frequently Asked Questions (3)
Can I compare GPT-4o mini 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-4o mini and MiniMax M2.7 today?
GPT-4o mini: $0.15 input / $0.60 output per 1M tokens 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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