Model comparison
GPT-5.3 Codex vs Qwen3.5 397B
Head-to-head evidence from 16 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); Qwen3.5 397B #71 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-5.3 Codex and Qwen3.5 397B share 16 comparable benchmark results. 2 of 8 categories are comparable. 5 results are unique to GPT-5.3 Codex; 39 to Qwen3.5 397B.
Updated July 20, 2026- Shared results
- 16
- GPT-5.3 Codex only
- 5
- Qwen3.5 397B only
- 39
- Comparable categories
- 2 / 8
Pick GPT-5.3 Codex if you want the stronger benchmark profile. Qwen3.5 397B only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 16 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
GPT-5.3 Codex is clearly ahead on the BenchAlign aggregate, 66.69 to 57.01. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.3 Codex's sharpest advantage is in agentic, where it averages 71.4 against 56.5. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 77.3% to 52.5%.
GPT-5.3 Codex is also the more expensive model on tokens at $1.75 input / $14.00 output per 1M tokens, versus $0.60 input / $3.60 output per 1M tokens for Qwen3.5 397B. That is roughly 3.9x on output cost alone. GPT-5.3 Codex is the reasoning model in the pair, while Qwen3.5 397B is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. GPT-5.3 Codex gives you the larger context window at 400K, compared with 128K for Qwen3.5 397B.
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 | Qwen3.5 397B |
|---|---|---|---|
| Agentic | GPT-5.3 Codex71.4 | Margin← 14.9 | Qwen3.5 397B56.5 |
| Coding | GPT-5.3 Codex67.2 | Margin← 0.7 | Qwen3.5 397B66.5 |
| Reasoning | GPT-5.3 CodexNot measured | MarginNo overlap | Qwen3.5 397B63.2 |
| Knowledge | GPT-5.3 CodexNot measured | MarginNo overlap | Qwen3.5 397B56.6 |
| Math | GPT-5.3 CodexNot measured | MarginNo overlap | Qwen3.5 397B90.6 |
| Multilingual | GPT-5.3 CodexNot measured | MarginNo overlap | Qwen3.5 397B84.7 |
| Multimodal | GPT-5.3 CodexNot measured | MarginNo overlap | Qwen3.5 397B79.6 |
| Inst. Following | GPT-5.3 CodexNot measured | MarginNo overlap | Qwen3.5 397B92.6 |
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 52.5%Winner: GPT-5.3 CodexΔ 24.8Terminal-Bench 2.0: GPT-5.3 Codex scored 77.3%; Qwen3.5 397B scored 52.5%. GPT-5.3 Codex wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 85%B 76.2%Winner: GPT-5.3 CodexΔ 8.8SWE-bench Verified: GPT-5.3 Codex scored 85%; Qwen3.5 397B scored 76.2%. GPT-5.3 Codex wins this benchmark. - Source ↗
SWE-bench Pro
CodingA 56.8%B 50.9%Winner: GPT-5.3 CodexΔ 5.9SWE-bench Pro: GPT-5.3 Codex scored 56.8%; Qwen3.5 397B scored 50.9%. 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 | Qwen3.5 397B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-5.3 Codex$1.75 input / $14 output | Qwen3.5 397B$0.6 input / $3.6 output | Qwen3.5 397B has the lower combined listed price. |
| Generation speedtokens per second | GPT-5.3 Codex79 tok/s | Qwen3.5 397B96 tok/s | Qwen3.5 397B has the higher measured throughput. |
| First-answer latencyseconds to first token | GPT-5.3 Codex88.26 s | Qwen3.5 397B2.44 s | Qwen3.5 397B reaches the first token sooner. |
| Context windowmaximum listed tokens | GPT-5.3 Codex400K | Qwen3.5 397B128K | GPT-5.3 Codex lists the larger context window. |
Benchmark Deep Dive
AgenticGPT-5.3 Codex wins20 benchmarks
| Benchmark | GPT-5.3 Codex | Qwen3.5 397B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 77.3% | 52.5% | GPT-5.3 Codex leads |
| OSWorld-VerifiedSource | 64.7% | — | Not comparable |
| τ²-bench resultsSource | 86% | 95.6% | Qwen3.5 397B leads |
| Gert LabsSource | 57.47% | 46.76% | GPT-5.3 Codex leads |
| JobBenchSource | 33.7% | — | Not comparable |
| BrowseCompSource | — | 62% | Not comparable |
| Claw-EvalSource | — | 56.8% | Not comparable |
| QwenClawBenchSource | — | 51.8% | Not comparable |
| τ³-bench resultsSource | — | 68.4% | Not comparable |
| VITA-BenchSource | — | 43.7% | Not comparable |
| DeepPlanningSource | — | 37.6% | Not comparable |
| ToolathlonSource | — | 36.3% | Not comparable |
| MCP AtlasSource | — | 46.1% | Not comparable |
| MCP-TasksSource | — | 74.2% | Not comparable |
| WideResearchSource | — | 74.0% | Not comparable |
| ResearchClawBenchSource | — | 14.2% | Not comparable |
| AA Agentic IndexSource | — | 19.9% | Not comparable |
| APEX-Agents-AASource | — | 15.3% | Not comparable |
| GDPval-AASource | — | 23.1% | Not comparable |
| GDPval-AASource | — | 962 | Not comparable |
CodingGPT-5.3 Codex wins7 benchmarks
| Benchmark | GPT-5.3 Codex | Qwen3.5 397B | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 85% | 76.2% | GPT-5.3 Codex leads |
| SWE-bench ProSource | 56.8% | 50.9% | GPT-5.3 Codex leads |
| SWE-RebenchSource | 58.2% | — | Not comparable |
| Vibe Code BenchSource | 61.77% | — | Not comparable |
| AA-SciCodeSource | 53.2% | 42.0% | GPT-5.3 Codex leads |
| LiveCodeBench v6Source | — | 83.6% | Not comparable |
| AA Coding IndexSource | — | 48.2% | Not comparable |
Reasoning4 benchmarks
Knowledge12 benchmarks
| Benchmark | GPT-5.3 Codex | Qwen3.5 397B | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 44.3% | 33.7% | GPT-5.3 Codex leads |
| AA-GPQA DiamondSource | 91.5% | 89.3% | GPT-5.3 Codex leads |
| AA-HLESource | 39.9% | 27.3% | GPT-5.3 Codex leads |
| AA-Omniscience IndexSource | 9.9% | -29.8% | GPT-5.3 Codex leads |
| AA-Omniscience AccuracySource | 51.8% | 31.4% | GPT-5.3 Codex leads |
| AA-Omniscience Hallucination RateSource | 86.9% | 89.1% | GPT-5.3 Codex leads |
| GPQASource | — | 88.4% | Not comparable |
| SuperGPQASource | — | 70.4% | Not comparable |
| MMLU-ProSource | — | 87.8% | Not comparable |
| MMLU-ReduxSource | — | 94.9% | Not comparable |
| C-EvalSource | — | 93% | Not comparable |
| HLESource | — | 28.7% | Not comparable |
Math5 benchmarks
Multilingual2 benchmarks
Multimodal8 benchmarks
| Benchmark | GPT-5.3 Codex | Qwen3.5 397B | Result |
|---|---|---|---|
| AA-MMMU-ProSource | 78.5% | 77.3% | GPT-5.3 Codex leads |
| Design Arena WebsiteSource | 1195 | — | Not comparable |
| MMMU-ProSource | — | 79% | Not comparable |
| MathVisionSource | — | 88.6% | Not comparable |
| CharXivSource | — | 80.8% | Not comparable |
| VideoMMMUSource | — | 84.7% | Not comparable |
| ScreenSpot ProSource | — | 65.6% | Not comparable |
| V*Source | — | 95.8% | Not comparable |
Frequently Asked Questions (3)
Which is better, GPT-5.3 Codex or Qwen3.5 397B?
GPT-5.3 Codex is ahead on BenchLM's BenchAlign leaderboard, 66.69 to 57.01. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 77.3% and 52.5%.
Which is better for coding, GPT-5.3 Codex or Qwen3.5 397B?
GPT-5.3 Codex has the edge for coding in this comparison, averaging 67.2 versus 66.5. 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 Qwen3.5 397B?
GPT-5.3 Codex has the edge for agentic tasks in this comparison, averaging 71.4 versus 56.5. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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