# GPT-5-Codex vs Qwen2.5 7B Instruct

> GPT-5-Codex is the stronger model overall, scoring 37.3 to 28.6 on the Noometry Index. Qwen2.5 7B Instruct costs 11× less per token, which makes it the better buy when GPT-5-Codex's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gpt-5-codex-vs-qwen2-5-7b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 0

## Summary

- The widest gap is in reasoning, where GPT-5-Codex leads 30.0 to 14.3.
- Qwen2.5 7B Instruct is cheaper at $0.17 / $0.70 per million input/output tokens, against $1.25 / $10 for GPT-5-Codex.
- GPT-5-Codex accepts more context: 400K tokens versus 131K.
- Qwen2.5 7B Instruct has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-5-Codex | Qwen2.5 7B Instruct |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 37.3 | 28.6 |
| Rank | 190 | 314 |
| Context | 400K | 131K |
| Input $/M | $1.25 | $0.17 |
| Output $/M | $10 | $0.70 |
| Weights | Proprietary | Open |

## Coding

- GPT-5-Codex: 41.9 (#100)
- Qwen2.5 7B Instruct: 35.9 (#204)

| Benchmark | GPT-5-Codex | Qwen2.5 7B Instruct |
|---|---|---|
| WeirdML | 54.5% | — |
| BigCodeBench Instruct | — | 37.6% |
| BigCodeBench Complete | — | 46.1% |

## Agentic & Tool Use

- GPT-5-Codex: 30.8 (#66)
- Qwen2.5 7B Instruct: 23.5 (#117)

| Benchmark | GPT-5-Codex | Qwen2.5 7B Instruct |
|---|---|---|
| Terminal-Bench | 44.3% | — |
| BALROG | — | 7.8% |

## Reasoning

- GPT-5-Codex: 30.0 (#80)
- Qwen2.5 7B Instruct: 14.3 (#317)

| Benchmark | GPT-5-Codex | Qwen2.5 7B Instruct |
|---|---|---|
| Kagi LLM Benchmark | 70.3% | — |
| Chess Puzzles | — | 0% |
| DTBench | — | 47.7% |
| LMCA | — | 6.4% |
| Epoch Capabilities Index | — | 118.44 |

## Math

- GPT-5-Codex: —
- Qwen2.5 7B Instruct: 11.8 (#300)

| Benchmark | GPT-5-Codex | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 2.5% |
| Omni-MATH | — | 29.4% |

## Knowledge

- GPT-5-Codex: —
- Qwen2.5 7B Instruct: 17.0 (#281)

| Benchmark | GPT-5-Codex | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | — | 35.5% |
| MMLU-Pro | — | 53.9% |
| GPQA (HELM) | — | 34.1% |
| MMLU | — | 72.9% |

## Instruction Following

- GPT-5-Codex: —
- Qwen2.5 7B Instruct: 63.4 (#226)

| Benchmark | GPT-5-Codex | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | — | 74.1% |

## Writing & Preference

- GPT-5-Codex: —
- Qwen2.5 7B Instruct: 48.8 (#194)

| Benchmark | GPT-5-Codex | Qwen2.5 7B Instruct |
|---|---|---|
| WildBench | — | 73.1% |

## FAQ

### Is GPT-5-Codex better than Qwen2.5 7B Instruct?

GPT-5-Codex is the stronger model overall, scoring 37.3 to 28.6 on the Noometry Index. Qwen2.5 7B Instruct costs 11× less per token, which makes it the better buy when GPT-5-Codex's lead doesn't matter for your workload.

### Which is cheaper, GPT-5-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-Codex lists at $1.25 and $10.

### Is GPT-5-Codex or Qwen2.5 7B Instruct better for coding?

GPT-5-Codex scores higher on coding benchmarks: 41.9 versus 35.9 in the Noometry coding category.

### Which has the bigger context window?

GPT-5-Codex does, with 400K tokens against 131K.

### How many benchmarks do GPT-5-Codex and Qwen2.5 7B Instruct share?

0 benchmarks have published results for both models. GPT-5-Codex has 3 scored results on Noometry and Qwen2.5 7B Instruct has 15.