Model comparison
GPT-5.3 Codex vs Mistral Medium 3.5 128B
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-5.3 Codex #30 (Supported); Mistral Medium 3.5 128B unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.3 Codex and Mistral Medium 3.5 128B share 14 comparable benchmark results. 1 of 8 categories are comparable. 7 results are unique to GPT-5.3 Codex; 11 to Mistral Medium 3.5 128B.
Updated July 27, 2026- Shared results
- 14
- GPT-5.3 Codex only
- 7
- Mistral Medium 3.5 128B only
- 11
- Comparable categories
- 1 / 8
Treat this as a split decision. GPT-5.3 Codex makes more sense if you need the larger 400K context window; Mistral Medium 3.5 128B is the better fit if coding is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 14 shared benchmark results across 6 evidence categories; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GPT-5.3 Codex and Mistral Medium 3.5 128B finish on the same BenchAlign overall score, so this is less about a single winner and more about where the edge shows up. The BenchAlign headline says tie; the benchmark table is where the real choice happens.
GPT-5.3 Codex is also the more expensive model on tokens at $1.75 input / $14.00 output per 1M tokens, versus $1.50 input / $7.50 output per 1M tokens for Mistral Medium 3.5 128B. GPT-5.3 Codex gives you the larger context window at 400K, compared with 256K for Mistral Medium 3.5 128B.
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 | Mistral Medium 3.5 128B |
|---|---|---|---|
| Coding | GPT-5.3 Codex67.2 | Margin→ 10.4 | Mistral Medium 3.5 128B77.6 |
| Agentic | GPT-5.3 Codex71.4 | MarginNo overlap | Mistral Medium 3.5 128BNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Verified
CodingA 85%B 77.6%Winner: GPT-5.3 CodexΔ 7.4SWE-bench Verified: GPT-5.3 Codex scored 85%; Mistral Medium 3.5 128B scored 77.6%. GPT-5.3 Codex 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 | Mistral Medium 3.5 128B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.3 Codex$1.75 input / $14 output | Mistral Medium 3.5 128B$1.5 input / $7.5 output | Mistral Medium 3.5 128B has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.3 Codex79 tok/s | Mistral Medium 3.5 128BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-5.3 Codex88.26 s | Mistral Medium 3.5 128BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-5.3 Codex400K | Mistral Medium 3.5 128B256K | GPT-5.3 Codex lists the larger context window. |
Benchmark Deep Dive
Agentic14 benchmarks
| Benchmark | GPT-5.3 Codex | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 77.3% | — | Not comparable |
| OSWorld-VerifiedSource | 64.7% | — | Not comparable |
| τ²-bench resultsSource | 86% | 94.2% | Mistral Medium 3.5 128B leads |
| Gert LabsSource | 57.47% | 39.10% | GPT-5.3 Codex leads |
| JobBenchSource | 33.7% | — | Not comparable |
| τ³-bench resultsSource | — | 91.4% | Not comparable |
| AA Agentic IndexSource | — | 19.0% | Not comparable |
| GDPval-AASource | — | 21.6% | Not comparable |
| GDPval-AASource | — | 933 | Not comparable |
| AA EnterpriseOps-GymSource | — | 33.7% | Not comparable |
| AA Harvey LABSource | — | 69.1% | Not comparable |
| terminalBenchHardSource | — | 33.3% | Not comparable |
| AA BriefcaseSource | — | 516 | Not comparable |
| AA Tau3 BankingSource | — | 14.4% | Not comparable |
CodingMistral Medium 3.5 128B wins6 benchmarks
| Benchmark | GPT-5.3 Codex | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 85% | 77.6% | GPT-5.3 Codex leads |
| SWE-bench ProSource | 56.8% | — | Not comparable |
| SWE-RebenchSource | 58.2% | — | Not comparable |
| Vibe Code BenchSource | 61.77% | — | Not comparable |
| AA-SciCodeSource | 53.2% | 39.6% | GPT-5.3 Codex leads |
| AA Coding IndexSource | — | 46.9% | Not comparable |
Reasoning2 benchmarks
Knowledge7 benchmarks
| Benchmark | GPT-5.3 Codex | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 44.3% | 29.9% | GPT-5.3 Codex leads |
| AA-GPQA DiamondSource | 91.5% | 74.8% | GPT-5.3 Codex leads |
| AA-HLESource | 39.9% | 12.8% | GPT-5.3 Codex leads |
| AA-Omniscience IndexSource | 9.9% | -36.3% | GPT-5.3 Codex leads |
| AA-Omniscience AccuracySource | 51.8% | 25.1% | GPT-5.3 Codex leads |
| AA-Omniscience Hallucination RateSource | 86.9% | 82.0% | Mistral Medium 3.5 128B leads |
| AA Openness IndexSource | — | 33.3% | Not comparable |
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | GPT-5.3 Codex | Mistral Medium 3.5 128B | Result |
|---|---|---|---|
| AA-IFBenchSource | 75.4% | 68.8% | GPT-5.3 Codex leads |
Frequently Asked Questions (2)
Which is better, GPT-5.3 Codex or Mistral Medium 3.5 128B?
GPT-5.3 Codex and Mistral Medium 3.5 128B are tied on the BenchAlign overall score, so the right pick depends on which category matters most for your use case.
Which is better for coding, GPT-5.3 Codex or Mistral Medium 3.5 128B?
Mistral Medium 3.5 128B has the edge for coding in this comparison, averaging 77.6 versus 67.2. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
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