Model comparison
Qwen2.5 7B Instruct vs Qwen3.5-9B
Qwen3.5-9B is the stronger model overall, scoring 33.8 to 29.0 on the Noometry Index.
Last verified . 6 shared benchmarks.
Alibaba (Qwen)Summary
- They share 6 benchmarks with published results for both. Qwen2.5 7B Instruct scores higher in 2 categories and Qwen3.5-9B in 3 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Qwen3.5-9B leads 46.0 to 17.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 2.5% for Qwen2.5 7B Instruct and 61.7% for Qwen3.5-9B.
- Qwen3.5-9B is cheaper at $0.10 / $0.15 per million input/output tokens, against $0.17 / $0.70 for Qwen2.5 7B Instruct.
- Qwen3.5-9B accepts more context: 262K tokens versus 131K.
Side by side
| Qwen2.5 7B Instruct | Qwen3.5-9B | |
|---|---|---|
| Provider | Alibaba (Qwen) | Alibaba (Qwen) |
| Noometry Index | 29.0 | 33.8 |
| Released | 2024-09 | 2026-02-23 |
| Weights | Open | Open |
| Context window | 131K | 262K |
| Max output | 8K | 66K |
| Input $ / M tokens | $0.17 | $0.10 |
| Output $ / M tokens | $0.70 | $0.15 |
| Results tracked | 15 | 10 |
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Category by category
Coding Too close to call
Qwen2.5 7B Instruct: 36.5 (#208), Qwen3.5-9B: 35.9 (#217)
| Benchmark | Qwen2.5 7B Instruct | Qwen3.5-9B |
|---|---|---|
| SciCode | — | 27.5% |
| BigCodeBench Instruct | 37.6% | — |
| BigCodeBench Complete | 46.1% | — |
Agentic & Tool Use Qwen2.5 7B Instruct leads
Qwen2.5 7B Instruct: 23.8 (#124), Qwen3.5-9B: 14.5 (#151)
| Benchmark | Qwen2.5 7B Instruct | Qwen3.5-9B |
|---|---|---|
| Terminal-Bench | — | 9.2% |
| BALROG | 7.8% | — |
Reasoning Qwen3.5-9B leads
Qwen2.5 7B Instruct: 14.8 (#322), Qwen3.5-9B: 23.1 (#182)
| Benchmark | Qwen2.5 7B Instruct | Qwen3.5-9B |
|---|---|---|
| Chess Puzzles | 0% | 12% |
| DTBench | 47.7% | 71.2% |
| LMCA | 6.4% | 24.5% |
| Epoch Capabilities Index | 118.51 | 139.46 |
| CritPt | — | 0.3% |
Math Qwen3.5-9B leads
Qwen2.5 7B Instruct: 12.6 (#306), Qwen3.5-9B: 34.8 (#192)
| Benchmark | Qwen2.5 7B Instruct | Qwen3.5-9B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 2.5% | 61.7% |
| MathArena Final-Answer Competitions | — | 48.5% |
| Omni-MATH | 29.4% | — |
Knowledge Qwen3.5-9B leads
Qwen2.5 7B Instruct: 17.0 (#286), Qwen3.5-9B: 46.0 (#84)
| Benchmark | Qwen2.5 7B Instruct | Qwen3.5-9B |
|---|---|---|
| GPQA Diamond | 35.5% | 79% |
| MMLU-Pro | 53.9% | — |
| GPQA (HELM) | 34.1% | — |
| MMLU | 72.9% | — |
Instruction Following Not comparable
Qwen2.5 7B Instruct: 63.2 (#231), Qwen3.5-9B: —
| Benchmark | Qwen2.5 7B Instruct | Qwen3.5-9B |
|---|---|---|
| IFEval | 74.1% | — |
Writing & Preference Not comparable
Qwen2.5 7B Instruct: 48.8 (#195), Qwen3.5-9B: —
| Benchmark | Qwen2.5 7B Instruct | Qwen3.5-9B |
|---|---|---|
| WildBench | 73.1% | — |
Frequently asked questions
Is Qwen2.5 7B Instruct better than Qwen3.5-9B?
Qwen3.5-9B is the stronger model overall, scoring 33.8 to 29.0 on the Noometry Index.
Which is cheaper, Qwen2.5 7B Instruct or Qwen3.5-9B?
Qwen3.5-9B is cheaper. It lists at $0.10 per million input tokens and $0.15 per million output tokens; Qwen2.5 7B Instruct lists at $0.17 and $0.70.
Is Qwen2.5 7B Instruct or Qwen3.5-9B better for coding?
They score almost the same on coding (36.5 vs 35.9); test both on your own repository before choosing.
Which has the bigger context window?
Qwen3.5-9B does, with 262K tokens against 131K.
How many benchmarks do Qwen2.5 7B Instruct and Qwen3.5-9B share?
6 benchmarks have published results for both models. Qwen2.5 7B Instruct has 15 scored results on Noometry and Qwen3.5-9B has 10.