Qwen3.5-9B vs Extract
Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.15
- Coding
- No data
- Overall
- No data
- Output $/M
- No data
Qwen3.5-9B or Extract: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
No benchmark has tested both yet. Qwen3.5-9B has results on AA Coding Index, SciCode and Terminal-Bench Hard (Artificial Analysis run); Extract has none yet. Compare them on price and context instead.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Qwen3.5-9B has a AA Intelligence Index result, 13.3.
Directional only
Long documents
Listed context window
Extract
Extract takes 1M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Extract
Qwen3.5-9B $0.0002, Extract $0.0000 per turn.
List prices
At a glance
- Coding index
- Qwen3.5-9B not benchmarked, Extract not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Qwen3.5-9B $0.15 against Extract Free per million output tokens.
- Context window
- Qwen3.5-9B takes 262K tokens, Extract 1M.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index4 boards measured
| Benchmark | Qwen3.5-9B | Extract | Leader |
|---|---|---|---|
| AA Coding Index | 23.5 | Not measured | Only Qwen3.5-9B measured |
| SciCode | 29.5% | Not measured | Only Qwen3.5-9B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 24.2% | Not measured | Only Qwen3.5-9B measured |
| Terminal-Bench | 9.2% | Not measured | Only Qwen3.5-9B measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Qwen3.5-9B | Extract | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.3 | Not measured | Only Qwen3.5-9B measured |
| Epoch Capabilities Index | 139.3 | Not measured | Only Qwen3.5-9B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Qwen3.5-9B | Extract | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 86.8% | Not measured | Only Qwen3.5-9B measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | Qwen3.5-9B | Extract | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 80.6% | Not measured | Only Qwen3.5-9B measured |
| Humanity's Last Exam (Artificial Analysis run) | 14.9% | Not measured | Only Qwen3.5-9B measured |
| GPQA Diamond (Epoch AI run) | 79.0% | Not measured | Only Qwen3.5-9B measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Qwen3.5-9B | Extract | Leader |
|---|---|---|---|
| OTIS Mock AIME | 61.7% | Not measured | Only Qwen3.5-9B measured |
| Neither model has a result on the other 12 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Qwen3.5-9B | Extract | Leader |
|---|---|---|---|
| IFBench | 66.7% | Not measured | Only Qwen3.5-9B measured |
| Long context reasoning (AA-LCR) | 70.0% | Not measured | Only Qwen3.5-9B measured |
| DTBench | 71.2% | Not measured | Only Qwen3.5-9B measured |
| LMCA | 24.5% | Not measured | Only Qwen3.5-9B measured |
| Neither model has a result on the other 42 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Extract costs less
Repository review
50K input + 3K output tokens
Extract costs less
Agent loop
200K input + 10K output tokens
Extract costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Qwen3.5-9B | ScaleDown Extract |
|---|---|---|
| Maker | Qwen | Scaledown |
| Weights | Open weights | No public weights listed |
| Released | March 10, 2026 | October 13, 2026 |
| Context window | 262K | 1M |
| Input $/Mtok | $0.10 | $0.04 |
| Output $/Mtok | $0.15 | Free |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Qwen3.5-9B better than Extract for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Qwen3.5-9B or Extract?
Qwen3.5-9B costs $0.10 per million input tokens and $0.15 per million output tokens; Extract costs $0.04 and Free. Cost per solved task is known for neither of them.
Which has the bigger context window, Qwen3.5-9B or Extract?
Extract, with 1M tokens against 262K.
Has any benchmark tested both Qwen3.5-9B and Extract?
No benchmark has tested both yet. Qwen3.5-9B has results on AA Coding Index, SciCode and Terminal-Bench Hard (Artificial Analysis run); Extract has none yet. Compare them on price and context instead. Data as of October 21, 2026.