Model comparison
GPT-5.3 Codex vs MiMo-V2.5-Pro
Head-to-head evidence from 15 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-5.3 Codex #26 (Supported); MiMo-V2.5-Pro #14 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.3 Codex and MiMo-V2.5-Pro share 15 comparable benchmark results. 2 of 8 categories are comparable. 6 results are unique to GPT-5.3 Codex; 16 to MiMo-V2.5-Pro.
Updated July 21, 2026- Shared results
- 15
- GPT-5.3 Codex only
- 6
- MiMo-V2.5-Pro only
- 16
- Comparable categories
- 2 / 8
Pick MiMo-V2.5-Pro if you want the stronger benchmark profile. GPT-5.3 Codex only becomes the better choice if coding is the priority.
Confidence note. This is a partial-evidence comparison with 15 shared benchmark results across 6 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
MiMo-V2.5-Pro is clearly ahead on the BenchAlign aggregate, 70.19 to 66.69. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiMo-V2.5-Pro gives you the larger context window at 1M, compared with 400K for GPT-5.3 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-5.3 Codex | Δ | MiMo-V2.5-Pro |
|---|---|---|---|
| Coding | GPT-5.3 Codex67.2 | Margin← 10.0 | MiMo-V2.5-Pro57.2 |
| Agentic | GPT-5.3 Codex71.4 | Margin← 3.0 | MiMo-V2.5-Pro68.4 |
| Knowledge | GPT-5.3 CodexNot measured | MarginNo overlap | MiMo-V2.5-Pro48.0 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 77.3%B 68.4%Winner: GPT-5.3 CodexΔ 8.9Terminal-Bench 2.0: GPT-5.3 Codex scored 77.3%; MiMo-V2.5-Pro scored 68.4%. GPT-5.3 Codex wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 56.8%B 57.2%Winner: MiMo-V2.5-ProΔ 0.4SWE-bench Pro: GPT-5.3 Codex scored 56.8%; MiMo-V2.5-Pro scored 57.2%. MiMo-V2.5-Pro wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-5.3 Codex | MiMo-V2.5-Pro | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.3 Codex$1.75 input / $14 output | MiMo-V2.5-ProNot available | A complete price comparison is not available. |
| Generation speedtokens per second | GPT-5.3 Codex79 tok/s | MiMo-V2.5-ProNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.3 Codex88.26 s | MiMo-V2.5-ProNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.3 Codex400K | MiMo-V2.5-Pro1M | MiMo-V2.5-Pro lists the larger context window. |
Benchmark Deep Dive
AgenticGPT-5.3 Codex wins16 benchmarks
| Benchmark | GPT-5.3 Codex | MiMo-V2.5-Pro | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 77.3% | 68.4% | GPT-5.3 Codex leads |
| OSWorld-VerifiedSource | 64.7% | — | Not comparable |
| τ²-bench resultsSource | 86% | 94.2% | MiMo-V2.5-Pro leads |
| Gert LabsSource | 57.47% | 62.70% | MiMo-V2.5-Pro leads |
| JobBenchSource | 33.7% | — | Not comparable |
| Claw-EvalSource | — | 63.8% | Not comparable |
| GDPval-AASource | — | 1265 | Not comparable |
| τ³-bench resultsSource | — | 72.9% | Not comparable |
| AA Agentic IndexSource | — | 29.1% | Not comparable |
| GDPval-AASource | — | 38.3% | Not comparable |
| APEX-Agents-AASource | — | 2.4% | Not comparable |
| AA BriefcaseSource | — | 873 | Not comparable |
| AA ITBenchSource | — | 38.2% | Not comparable |
| terminalBenchHardSource | — | 43.2% | Not comparable |
| aaTerminalBench21Source | — | 65.2% | Not comparable |
| AA Harvey LABSource | — | 73.3% | Not comparable |
CodingGPT-5.3 Codex wins7 benchmarks
| Benchmark | GPT-5.3 Codex | MiMo-V2.5-Pro | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 85% | — | Not comparable |
| SWE-bench ProSource | 56.8% | 57.2% | MiMo-V2.5-Pro leads |
| SWE-RebenchSource | 58.2% | — | Not comparable |
| Vibe Code BenchSource | 61.77% | — | Not comparable |
| AA-SciCodeSource | 53.2% | 50.2% | GPT-5.3 Codex leads |
| Terminal-Bench 2.0Source | — | 68.4% | Not comparable |
| AA Coding IndexSource | — | 60.2% | Not comparable |
Reasoning2 benchmarks
Knowledge9 benchmarks
| Benchmark | GPT-5.3 Codex | MiMo-V2.5-Pro | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 44.3% | 42.2% | GPT-5.3 Codex leads |
| AA-GPQA DiamondSource | 91.5% | 86.6% | GPT-5.3 Codex leads |
| AA-HLESource | 39.9% | 33.8% | GPT-5.3 Codex leads |
| AA-Omniscience IndexSource | 9.9% | 3.6% | GPT-5.3 Codex leads |
| AA-Omniscience AccuracySource | 51.8% | 22.6% | GPT-5.3 Codex leads |
| AA-Omniscience Hallucination RateSource | 86.9% | 24.5% | MiMo-V2.5-Pro leads |
| HLESource | — | 48% | Not comparable |
| HLE w/o toolsSource | — | 34% | Not comparable |
| AA Openness IndexSource | — | 38.9% | Not comparable |
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | GPT-5.3 Codex | MiMo-V2.5-Pro | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.4% | 79.9% | MiMo-V2.5-Pro leads |
Frequently Asked Questions (3)
Which is better, GPT-5.3 Codex or MiMo-V2.5-Pro?
MiMo-V2.5-Pro is ahead on BenchLM's BenchAlign leaderboard, 70.19 to 66.69. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 77.3% and 68.4%.
Which is better for coding, GPT-5.3 Codex or MiMo-V2.5-Pro?
GPT-5.3 Codex has the edge for coding in this comparison, averaging 67.2 versus 57.2. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GPT-5.3 Codex or MiMo-V2.5-Pro?
GPT-5.3 Codex has the edge for agentic tasks in this comparison, averaging 71.4 versus 68.4. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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