Model comparison
GPT-OSS 20B vs MiniMax M3
Head-to-head evidence from 17 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Verified leaderboard positions: GPT-OSS 20B unranked; MiniMax M3 #18
BenchAlign evidence: GPT-OSS 20B supported; MiniMax M3 supported. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-OSS 20B and MiniMax M3 share 17 comparable benchmark results. 0 of 8 categories are comparable. 2 results are unique to GPT-OSS 20B; 28 to MiniMax M3.
Updated July 16, 2026- Shared results
- 17
- GPT-OSS 20B only
- 2
- MiniMax M3 only
- 28
- Comparable categories
- 0 / 8
Benchmark data for GPT-OSS 20B and MiniMax M3 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 17 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.
MiniMax M3 is priced at $0.30 input / $1.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for GPT-OSS 20B. MiniMax M3 has the larger context window at 1M, compared with 128K for GPT-OSS 20B.
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-OSS 20B | Δ | MiniMax M3 |
|---|---|---|---|
| Agentic | GPT-OSS 20BNot measured | MarginNo overlap | MiniMax M372.3 |
| Coding | GPT-OSS 20BNot measured | MarginNo overlap | MiniMax M372.2 |
| Math | GPT-OSS 20BNot measured | MarginNo overlap | MiniMax M385.7 |
| Multimodal | GPT-OSS 20BNot 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-OSS 20B | MiniMax M3 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-OSS 20B$0 input / $0 output | MiniMax M3$0.3 input / $1.2 output | GPT-OSS 20B has the lower combined listed price. |
| Generation speedtokens per second | GPT-OSS 20B313 tok/s | MiniMax M3Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-OSS 20B0.65 s | MiniMax M3Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-OSS 20B128K | MiniMax M31M | MiniMax M3 lists the larger context window. |
Benchmark Deep Dive
Agentic17 benchmarks
| Benchmark | GPT-OSS 20B | MiniMax M3 | Result |
|---|---|---|---|
| AA Agentic IndexSource | 3.1% | 35.4% | MiniMax M3 leads |
| APEX-Agents-AASource | 0.7% | — | Not comparable |
| τ²-bench resultsSource | 60.2% | 88.9% | MiniMax M3 leads |
| GDPval-AASource | 3.0% | 44.7% | MiniMax M3 leads |
| GDPval-AASource | 559 | 1395 | MiniMax M3 leads |
| 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 |
| 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-OSS 20B | MiniMax M3 | Result |
|---|---|---|---|
| React Native EvalsSource | 71% | — | Not comparable |
| AA Coding IndexSource | 20.7% | 58.6% | MiniMax M3 leads |
| Terminal-Bench HardSource | 10.6% | 42.4% | MiniMax M3 leads |
| AA-SciCodeSource | 34.4% | 45.4% | MiniMax M3 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 |
| 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-OSS 20B | MiniMax M3 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 14.9% | 44.4% | MiniMax M3 leads |
| AA-GPQA DiamondSource | 68.8% | 92.9% | MiniMax M3 leads |
| AA-HLESource | 9.8% | 37.1% | MiniMax M3 leads |
| AA-Omniscience IndexSource | -63.9% | 1.4% | MiniMax M3 leads |
| AA-Omniscience AccuracySource | 15.5% | 15.0% | GPT-OSS 20B leads |
| AA-Omniscience Hallucination RateSource | 94.1% | 16.1% | MiniMax M3 leads |
| AA Openness IndexSource | — | 33.3% | Not comparable |
Math1 benchmarks
| Benchmark | GPT-OSS 20B | MiniMax M3 | Result |
|---|---|---|---|
| USAMO 2026Source | — | 85.7% | Not comparable |
Multimodal7 benchmarks
| Benchmark | GPT-OSS 20B | MiniMax M3 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 887 | 1294 | 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 |
| AA-MMMU-ProSource | — | 78.6% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GPT-OSS 20B | MiniMax M3 | Result |
|---|---|---|---|
| AA-IFBenchSource | 65.1% | 82.9% | MiniMax M3 leads |
Frequently Asked Questions (3)
Can I compare GPT-OSS 20B 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-OSS 20B and MiniMax M3 today?
GPT-OSS 20B: $0.00 input / $0.00 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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