Model comparison
GPT-5.2 Codex vs Qwen2.5 7B Instruct
GPT-5.2 Codex is the stronger model overall, scoring 42.6 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 16× less per token, which makes it the better buy when GPT-5.2 Codex's lead doesn't matter for your workload.
Last verified . 0 shared benchmarks.
Alibaba (Qwen)Summary
- The widest gap is in agentic & tool use, where GPT-5.2 Codex leads 41.0 to 23.8.
- Qwen2.5 7B Instruct is cheaper at $0.17 / $0.70 per million input/output tokens, against $1.75 / $14 for GPT-5.2 Codex.
- GPT-5.2 Codex accepts more context: 400K tokens versus 131K.
- Qwen2.5 7B Instruct has downloadable open weights; the other is API-only.
Side by side
| GPT-5.2 Codex | Qwen2.5 7B Instruct | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 42.6 | 29.0 |
| Released | 2025-12-18 | 2024-09 |
| Weights | Proprietary | Open |
| Context window | 400K | 131K |
| Max output | 128K | 8K |
| Input $ / M tokens | $1.75 | $0.17 |
| Output $ / M tokens | $14 | $0.70 |
| Results tracked | 5 | 15 |
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Category by category
Coding GPT-5.2 Codex leads
GPT-5.2 Codex: 45.5 (#71), Qwen2.5 7B Instruct: 36.5 (#208)
| Benchmark | GPT-5.2 Codex | Qwen2.5 7B Instruct |
|---|---|---|
| SWE-bench Verified (bash only) | 72.8% | — |
| LMArena WebDev | 1339 | — |
| SWE-bench Multilingual | 66.3% | — |
| BigCodeBench Instruct | — | 37.6% |
| BigCodeBench Complete | — | 46.1% |
| ALE-Bench | 1,300 | — |
Agentic & Tool Use GPT-5.2 Codex leads
GPT-5.2 Codex: 41.0 (#22), Qwen2.5 7B Instruct: 23.8 (#124)
| Benchmark | GPT-5.2 Codex | Qwen2.5 7B Instruct |
|---|---|---|
| Terminal-Bench | 66.5% | — |
| BALROG | — | 7.8% |
Reasoning Not comparable
GPT-5.2 Codex: —, Qwen2.5 7B Instruct: 14.8 (#322)
| Benchmark | GPT-5.2 Codex | Qwen2.5 7B Instruct |
|---|---|---|
| Chess Puzzles | — | 0% |
| DTBench | — | 47.7% |
| LMCA | — | 6.4% |
| Epoch Capabilities Index | — | 118.51 |
Math Not comparable
GPT-5.2 Codex: —, Qwen2.5 7B Instruct: 12.6 (#306)
| Benchmark | GPT-5.2 Codex | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 2.5% |
| Omni-MATH | — | 29.4% |
Knowledge Not comparable
GPT-5.2 Codex: —, Qwen2.5 7B Instruct: 17.0 (#286)
| Benchmark | GPT-5.2 Codex | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | — | 35.5% |
| MMLU-Pro | — | 53.9% |
| GPQA (HELM) | — | 34.1% |
| MMLU | — | 72.9% |
Instruction Following Not comparable
GPT-5.2 Codex: —, Qwen2.5 7B Instruct: 63.2 (#231)
| Benchmark | GPT-5.2 Codex | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | — | 74.1% |
Writing & Preference Not comparable
GPT-5.2 Codex: —, Qwen2.5 7B Instruct: 48.8 (#195)
| Benchmark | GPT-5.2 Codex | Qwen2.5 7B Instruct |
|---|---|---|
| WildBench | — | 73.1% |
Frequently asked questions
Is GPT-5.2 Codex better than Qwen2.5 7B Instruct?
GPT-5.2 Codex is the stronger model overall, scoring 42.6 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 16× less per token, which makes it the better buy when GPT-5.2 Codex's lead doesn't matter for your workload.
Which is cheaper, GPT-5.2 Codex or Qwen2.5 7B Instruct?
Qwen2.5 7B Instruct is cheaper. It lists at $0.17 per million input tokens and $0.70 per million output tokens; GPT-5.2 Codex lists at $1.75 and $14.
Is GPT-5.2 Codex or Qwen2.5 7B Instruct better for coding?
GPT-5.2 Codex scores higher on coding benchmarks: 45.5 versus 36.5 in the Noometry coding category.
Which has the bigger context window?
GPT-5.2 Codex does, with 400K tokens against 131K.
How many benchmarks do GPT-5.2 Codex and Qwen2.5 7B Instruct share?
0 benchmarks have published results for both models. GPT-5.2 Codex has 5 scored results on Noometry and Qwen2.5 7B Instruct has 15.