Compare · ModelsLive · 2 picked · head to head
GPT-5.3-Codex vs Qwen3.7 Plus
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
GPT-5.3-Codex wins on 7/11 benchmarks
GPT-5.3-Codex wins 7 of 11 shared benchmarks. Leads in speed.
Category leads
speed·GPT-5.3-Codex
Hype vs Reality
Attention vs performance
GPT-5.3-Codex
#73 by perf·#4 by attention
Qwen3.7 Plus
#183 by perf·#2 by attention
Best value
Qwen3.7 Plus
7.3x better value than GPT-5.3-Codex
GPT-5.3-Codex
7.1 pts/$
$7.88/M
Qwen3.7 Plus
51.5 pts/$
$0.80/M
Vendor risk
Who is behind the model
OpenAI
$840.0B·Tier 1
Alibaba (Qwen)
$293.0B·Tier 1
Head to head
11 benchmarks · 2 models
GPT-5.3-CodexQwen3.7 Plus
Artificial Analysis · Agentic Index
GPT-5.3-Codex leads by +41.4
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 Plus
20.8
Artificial Analysis · Coding Index
Qwen3.7 Plus leads by +2.8
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 Plus
55.9
Artificial Analysis · CritPt
GPT-5.3-Codex leads by +7.8
GPT-5.3-Codex
16.9
Qwen3.7 Plus
9.1
Artificial Analysis · GPQA Diamond
GPT-5.3-Codex leads by +1.5
GPT-5.3-Codex
91.5
Qwen3.7 Plus
90.0
Artificial Analysis · Humanity's Last Exam
GPT-5.3-Codex leads by +6.9
GPT-5.3-Codex
42.5
Qwen3.7 Plus
35.6
Artificial Analysis · IFBench
Qwen3.7 Plus leads by +2.6
GPT-5.3-Codex
75.4
Qwen3.7 Plus
78.0
Artificial Analysis · Long Context Reasoning
GPT-5.3-Codex leads by +10.3
GPT-5.3-Codex
83.3
Qwen3.7 Plus
73.0
Artificial Analysis · MMMU Pro
Qwen3.7 Plus leads by +2.0
GPT-5.3-Codex
78.5
Qwen3.7 Plus
80.5
Artificial Analysis · Quality Index
GPT-5.3-Codex leads by +7.3
GPT-5.3-Codex
32.5
Qwen3.7 Plus
25.2
Artificial Analysis · tau2-Bench Telecom
Qwen3.7 Plus leads by +7.0
GPT-5.3-Codex
86.0
Qwen3.7 Plus
93.0
Artificial Analysis · Terminal-Bench Hard
GPT-5.3-Codex leads by +6.0
GPT-5.3-Codex
53.0
Qwen3.7 Plus
47.0
Full benchmark table
| Benchmark | GPT-5.3-Codex | Qwen3.7 Plus |
|---|---|---|
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 | 20.8 |
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 | 55.9 |
Artificial Analysis · CritPt | 16.9 | 9.1 |
Artificial Analysis · GPQA Diamond | 91.5 | 90.0 |
Artificial Analysis · Humanity's Last Exam | 42.5 | 35.6 |
Artificial Analysis · IFBench | 75.4 | 78.0 |
Artificial Analysis · Long Context Reasoning | 83.3 | 73.0 |
Artificial Analysis · MMMU Pro | 78.5 | 80.5 |
Artificial Analysis · Quality Index | 32.5 | 25.2 |
Artificial Analysis · tau2-Bench Telecom | 86.0 | 93.0 |
Artificial Analysis · Terminal-Bench Hard | 53.0 | 47.0 |
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 | |
| $0.32 | $1.28 | 1.0M tokens (~500 books) | $5.60 |