Model comparison
GPT-5.2-Codex vs MiniMax M3
Head-to-head evidence from 13 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Verified leaderboard positions: GPT-5.2-Codex unranked; MiniMax M3 #18
BenchAlign evidence: GPT-5.2-Codex supported; MiniMax M3 supported. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.2-Codex and MiniMax M3 share 13 comparable benchmark results. 0 of 8 categories are comparable. 3 results are unique to GPT-5.2-Codex; 32 to MiniMax M3.
Updated July 16, 2026- Shared results
- 13
- GPT-5.2-Codex only
- 3
- MiniMax M3 only
- 32
- Comparable categories
- 0 / 8
Benchmark data for GPT-5.2-Codex and MiniMax M3 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 13 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-5.2-Codex is priced at $1.75 input / $14.00 output per 1M tokens, versus $0.30 input / $1.20 output per 1M tokens for MiniMax M3. MiniMax M3 has the larger context window at 1M, compared with 400K for GPT-5.2-Codex.
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-5.2-Codex | Δ | MiniMax M3 |
|---|---|---|---|
| Agentic | GPT-5.2-CodexNot measured | MarginNo overlap | MiniMax M372.3 |
| Coding | GPT-5.2-CodexNot measured | MarginNo overlap | MiniMax M372.2 |
| Math | GPT-5.2-CodexNot measured | MarginNo overlap | MiniMax M385.7 |
| Multimodal | GPT-5.2-CodexNot measured | MarginNo overlap | MiniMax M364.9 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.2-Codex | MiniMax M3 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.2-Codex$1.75 input / $14 output | MiniMax M3$0.3 input / $1.2 output | MiniMax M3 has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.2-Codex123 tok/s | MiniMax M3Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.2-Codex87.34 s | MiniMax M3Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.2-Codex400K | MiniMax M31M | MiniMax M3 lists the larger context window. |
Benchmark Deep Dive
Agentic18 benchmarks
| Benchmark | GPT-5.2-Codex | MiniMax M3 | Result |
|---|---|---|---|
| τ²-bench resultsSource | 92.1% | 88.9% | GPT-5.2-Codex leads |
| Gert LabsSource | 51.79% | — | Not comparable |
| JobBenchSource | 26.0% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 66% | Not comparable |
| BrowseCompSource | — | 83.5% | Not comparable |
| OSWorld-VerifiedSource | — | 70.1% | Not comparable |
| MCP AtlasSource | — | 74.2% | Not comparable |
| Claw-EvalSource | — | 74.5% | Not comparable |
| AA Agentic IndexSource | — | 35.4% | Not comparable |
| GDPval-AASource | — | 44.7% | Not comparable |
| GDPval-AASource | — | 1395 | Not comparable |
| GDPval rubricsSource | — | 74.7% | Not comparable |
| BankerToolBenchSource | — | 76.1% | Not comparable |
| ResearchClawBenchSource | — | 19.8% | Not comparable |
| OSWorld 2.0Source | — | 4.6% | Not comparable |
| AA BriefcaseSource | — | 1110 | Not comparable |
| AA EnterpriseOps-GymSource | — | 32.1% | Not comparable |
| AA Harvey LABSource | — | 6.7% | Not comparable |
Coding12 benchmarks
| Benchmark | GPT-5.2-Codex | MiniMax M3 | Result |
|---|---|---|---|
| Vibe Code BenchSource | 37.91% | — | Not comparable |
| Terminal-Bench HardSource | 37.1% | 42.4% | MiniMax M3 leads |
| AA-SciCodeSource | 54.6% | 45.4% | GPT-5.2-Codex leads |
| SWE-bench VerifiedSource | — | 80.5% | Not comparable |
| SWE-bench ProSource | — | 59% | Not comparable |
| Terminal-Bench 2.0Source | — | 66.0% | Not comparable |
| NL2RepoSource | — | 42.1% | Not comparable |
| AA Coding IndexSource | — | 58.6% | Not comparable |
| VIBE V2Source | — | 50.1% | Not comparable |
| SVG-BenchSource | — | 63.7% | Not comparable |
| KernelBench HardSource | — | 28.8% | Not comparable |
| AA Terminal-Bench 2.1Source | — | 65.2% | Not comparable |
Reasoning2 benchmarks
Knowledge7 benchmarks
| Benchmark | GPT-5.2-Codex | MiniMax M3 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 40.1% | 44.4% | MiniMax M3 leads |
| AA-GPQA DiamondSource | 89.9% | 92.9% | MiniMax M3 leads |
| AA-HLESource | 33.5% | 37.1% | MiniMax M3 leads |
| AA-Omniscience IndexSource | -2.5% | 1.4% | MiniMax M3 leads |
| AA-Omniscience AccuracySource | 40.7% | 15.0% | GPT-5.2-Codex leads |
| AA-Omniscience Hallucination RateSource | 72.8% | 16.1% | MiniMax M3 leads |
| AA Openness IndexSource | — | 33.3% | Not comparable |
Math1 benchmarks
| Benchmark | GPT-5.2-Codex | MiniMax M3 | Result |
|---|---|---|---|
| USAMO 2026Source | — | 85.7% | Not comparable |
Multimodal7 benchmarks
| Benchmark | GPT-5.2-Codex | MiniMax M3 | Result |
|---|---|---|---|
| AA-MMMU-ProSource | 76.3% | 78.6% | MiniMax M3 leads |
| OfficeQA ProSource | — | 45.1% | Not comparable |
| OmniDocBench 1.5Source | — | 91.6% | Not comparable |
| MMMU-ProSource | — | 78.1% | Not comparable |
| VideoMMMUSource | — | 84.6% | Not comparable |
| Video-MME (with subtitle)Source | — | 85.4% | Not comparable |
| Design Arena WebsiteSource | — | 1294 | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GPT-5.2-Codex | MiniMax M3 | Result |
|---|---|---|---|
| AA-IFBenchSource | 77.6% | 82.9% | MiniMax M3 leads |
Frequently Asked Questions (3)
Can I compare GPT-5.2-Codex and MiniMax M3 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-5.2-Codex and MiniMax M3 today?
GPT-5.2-Codex: $1.75 input / $14.00 output per 1M tokens MiniMax M3: $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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