Compare · ModelsLive · 2 picked · head to head
GPT-5.3-Codex vs Qwen3.7 Max
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
Qwen3.7 Max wins on 3/4 benchmarks
Qwen3.7 Max wins 3 of 4 shared benchmarks. Leads in speed · coding.
Category leads
speed·Qwen3.7 Maxcoding·Qwen3.7 Max
Hype vs Reality
Attention vs performance
GPT-5.3-Codex
#83 by perf·#9 by attention
Qwen3.7 Max
#45 by perf·#1 by attention
Best value
Qwen3.7 Max
3.0x better value than GPT-5.3-Codex
GPT-5.3-Codex
7.0 pts/$
$7.88/M
Qwen3.7 Max
21.1 pts/$
$2.95/M
Vendor risk
Who is behind the model
OpenAI
$840.0B·Tier 1
Alibaba (Qwen)
$293.0B·Tier 1
Head to head
4 benchmarks · 2 models
GPT-5.3-CodexQwen3.7 Max
Artificial Analysis · Agentic Index
GPT-5.3-Codex leads by +31.6
Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?"
GPT-5.3-Codex
62.2
Qwen3.7 Max
30.6
Artificial Analysis · Coding Index
Qwen3.7 Max leads by +12.9
Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads.
GPT-5.3-Codex
53.1
Qwen3.7 Max
66.0
Artificial Analysis · Quality Index
Qwen3.7 Max leads by +13.5
GPT-5.3-Codex
32.5
Qwen3.7 Max
46.0
SWE-Bench verified
Qwen3.7 Max leads by +2.5
SWE-bench Verified · 500 human-validated tasks from 12 real Python repositories (Django, Flask, scikit-learn, sympy, and others). Each task requires the model to produce a git patch that resolves a real GitHub issue and passes the test suite. The verified subset eliminates ambiguous tasks from the original SWE-bench. The benchmark remains the most-cited evaluation for code-generation capability.
GPT-5.3-Codex
74.8
Qwen3.7 Max
77.3
Full benchmark table
| Benchmark | GPT-5.3-Codex | Qwen3.7 Max |
|---|---|---|
Artificial Analysis · Agentic Index Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?" | 62.2 | 30.6 |
Artificial Analysis · Coding Index Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads. | 53.1 | 66.0 |
Artificial Analysis · Quality Index | 32.5 | 46.0 |
SWE-Bench verified SWE-bench Verified · 500 human-validated tasks from 12 real Python repositories (Django, Flask, scikit-learn, sympy, and others). Each task requires the model to produce a git patch that resolves a real GitHub issue and passes the test suite. The verified subset eliminates ambiguous tasks from the original SWE-bench. The benchmark remains the most-cited evaluation for code-generation capability. | 74.8 | 77.3 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $1.75 | $14.00 | 400K tokens (~200 books) | $48.13 | |
| $1.48 | $4.42 | 1.0M tokens (~500 books) | $22.13 |