Model comparison
GPT-5-Codex vs Qwen3-1.7B
GPT-5-Codex is the stronger model overall, scoring 37.3 to 26.2 on the Noometry Index.
Last verified . 0 shared benchmarks.
Alibaba (Qwen)Summary
- The widest gap is in reasoning, where GPT-5-Codex leads 30.0 to 18.5.
- Qwen3-1.7B has downloadable open weights; the other is API-only.
Side by side
| GPT-5-Codex | Qwen3-1.7B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 37.3 | 26.2 |
| Released | 2025-09-15 | 2025-04-29 |
| Weights | Proprietary | Open |
| Context window | 400K | — |
| Max output | 128K | — |
| Input $ / M tokens | $1.25 | — |
| Output $ / M tokens | $10 | — |
| Results tracked | 3 | 4 |
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Category by category
Coding Not comparable
GPT-5-Codex: 41.9 (#100), Qwen3-1.7B: —
| Benchmark | GPT-5-Codex | Qwen3-1.7B |
|---|---|---|
| WeirdML | 54.5% | — |
Agentic & Tool Use GPT-5-Codex leads
GPT-5-Codex: 30.8 (#66), Qwen3-1.7B: 24.5 (#108)
| Benchmark | GPT-5-Codex | Qwen3-1.7B |
|---|---|---|
| Terminal-Bench | 44.3% | — |
| Berkeley Function Calling Leaderboard | — | 28.4% |
Reasoning GPT-5-Codex leads
GPT-5-Codex: 30.0 (#80), Qwen3-1.7B: 18.5 (#263)
| Benchmark | GPT-5-Codex | Qwen3-1.7B |
|---|---|---|
| Kagi LLM Benchmark | 70.3% | — |
| Chess Puzzles | — | 0% |
Math Not comparable
GPT-5-Codex: —, Qwen3-1.7B: 15.4 (#288)
| Benchmark | GPT-5-Codex | Qwen3-1.7B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 8.1% |
Knowledge Not comparable
GPT-5-Codex: —, Qwen3-1.7B: 19.6 (#273)
| Benchmark | GPT-5-Codex | Qwen3-1.7B |
|---|---|---|
| GPQA Diamond | — | 38% |
Frequently asked questions
Is GPT-5-Codex better than Qwen3-1.7B?
GPT-5-Codex is the stronger model overall, scoring 37.3 to 26.2 on the Noometry Index.
How many benchmarks do GPT-5-Codex and Qwen3-1.7B share?
0 benchmarks have published results for both models. GPT-5-Codex has 3 scored results on Noometry and Qwen3-1.7B has 4.