Mercury 2.5 vs Qwen3.5-9B
Qwen3.5-9B leads Mercury 2.5 on the Artificial Analysis Intelligence Index, 13.3 to 12.3, at about the same output price ($0.15 vs $0.15 per million). Updated October 22, 2026; 4 benchmarks have a result for both.
Mercury 2.5 or Qwen3.5-9B: which to pick
Only Mercury 2.5 has a coding score, 1.6 on the Vibe Coding Index; Qwen3.5-9B has none, so the two cannot be ranked on coding.
Pick Qwen3.5-9B for a larger context window (262K against 260K tokens) and work measured by AA Intelligence Index or Humanity's Last Exam (Artificial Analysis run), where it leads.
- Shared results
- 4
- Mercury 2.5 only
- 18
- Qwen3.5-9B only
- 11
- Like-for-like categories
- 4 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
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
Only Mercury 2.5 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.5-9B
Qwen3.5-9B leads, 13.3 against 12.3.
Same board, both measured
Long documents
Listed context window
Qwen3.5-9B
Qwen3.5-9B takes 262K tokens against 260K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mercury 2.5
Mercury 2.5 $0.0001, Qwen3.5-9B $0.0002 per turn.
List prices
At a glance
- Coding index
- Mercury 2.5 1.6 from 2 benchmarks, Qwen3.5-9B not benchmarked.
- Widest category
- Coding, 9.0 points apart on average over 1 shared benchmark.
- Widest single gap
- SciCode: Mercury 2.5 38.5% against Qwen3.5-9B 29.5%, 9.0 points apart on a 0 to 100 scale.
- Output price
- Both cost $0.15 per million output tokens.
- Context window
- Mercury 2.5 takes 260K tokens, Qwen3.5-9B 262K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | Mercury 2.5 | Bars | Qwen3.5-9B | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| General | 1 | 0 | ||
| All shared boards | 2 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | Mercury 2.5 | Qwen3.5-9B | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 23.5 | Only Qwen3.5-9B measured |
| Terminal-Bench 4 (Vals AI) | 0.0% | Not measured | Only Mercury 2.5 measured |
| Vibe Code Bench (Vals AI) | 3.7% | Not measured | Only Mercury 2.5 measured |
| Code migration (Vals AI) | 4.5% | Not measured | Only Mercury 2.5 measured |
| IOI (Vals AI) | 2.4% | Not measured | Only Mercury 2.5 measured |
| Terminal-Bench 2.1 (Vals AI) | 34.5% | Not measured | Only Mercury 2.5 measured |
| SkillsBench (Vals AI) | 18.1% | Not measured | Only Mercury 2.5 measured |
| CyberBench (Vals AI) | 45.5% | Not measured | Only Mercury 2.5 measured |
| SciCode | 38.5% | 29.5% | Mercury 2.5 |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 24.2% | Only Qwen3.5-9B measured |
| Terminal-Bench | Not measured | 9.2% | Only Qwen3.5-9B measured |
| ALE-Bench | 301.6 | Not measured | Only Mercury 2.5 measured |
| Neither model has a result on the other 27 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mercury 2.5 | Qwen3.5-9B | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.3 | 13.3 | Qwen3.5-9B |
| Epoch Capabilities Index | Not measured | 139.3 | Only Qwen3.5-9B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Mercury 2.5 | Qwen3.5-9B | Leader |
|---|---|---|---|
| Finance Agent (Vals AI) | 18.9% | Not measured | Only Mercury 2.5 measured |
| Tax Agent Bench (Vals AI) | 2.5% | Not measured | Only Mercury 2.5 measured |
| Legal Research Bench (Vals AI) | 4.3% | Not measured | Only Mercury 2.5 measured |
| Excel Modeling Benchmark (Vals AI) | 8.8% | Not measured | Only Mercury 2.5 measured |
| Terminal-Bench-Science (Vals AI) | 0.0% | Not measured | Only Mercury 2.5 measured |
| τ²-Bench Telecom | Not measured | 86.8% | Only Qwen3.5-9B measured |
| Neither model has a result on the other 35 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mercury 2.5 | Qwen3.5-9B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 80.6% | Only Qwen3.5-9B measured |
| Humanity's Last Exam (Artificial Analysis run) | 11.8% | 14.9% | Qwen3.5-9B |
| GPQA Diamond (Epoch AI run) | Not measured | 79.0% | 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 index2 boards, none shared
| Benchmark | Mercury 2.5 | Qwen3.5-9B | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 61.7% | Only Qwen3.5-9B measured |
| ProofBench | 3.0% | Not measured | Only Mercury 2.5 measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Mercury 2.5 | Qwen3.5-9B | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | 83.1% | Not measured | Only Mercury 2.5 measured |
| MedCode (Vals AI) | 31.3% | Not measured | Only Mercury 2.5 measured |
| Neither model has a result on the other 16 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mercury 2.5 | Qwen3.5-9B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Mercury 2.5 | Qwen3.5-9B | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 45.3% | Not measured | Only Mercury 2.5 measured |
| MedScribe (Vals AI) | 55.1% | Not measured | Only Mercury 2.5 measured |
| IFBench | Not measured | 66.7% | Only Qwen3.5-9B measured |
| Long context reasoning (AA-LCR) | 71.7% | 70.0% | Mercury 2.5 |
| DTBench | Not measured | 71.2% | Only Qwen3.5-9B measured |
| LMCA | Not measured | 24.5% | Only Qwen3.5-9B measured |
| Neither model has a result on the other 40 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mercury 2.5 | Qwen3.5-9B | Leader |
|---|---|---|---|
| Neither model has a result on 5 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
Mercury 2.5 costs less
Repository review
50K input + 3K output tokens
Mercury 2.5 costs less
Agent loop
200K input + 10K output tokens
Mercury 2.5 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 | Inception Mercury 2.5 | Qwen3.5-9B |
|---|---|---|
| Maker | Inception | Qwen |
| Weights | No public weights listed | Open weights |
| Released | September 8, 2026 | March 10, 2026 |
| Context window | 260K | 262K |
| Input $/Mtok | $0.04 | $0.10 |
| Output $/Mtok | $0.15 | $0.15 |
| Coding index | 1.6 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mercury 2.5 better than Qwen3.5-9B for coding?
Only Mercury 2.5 has a coding score, 1.6 on the Vibe Coding Index; Qwen3.5-9B has none, so the two cannot be ranked on coding. Pick Qwen3.5-9B for a larger context window (262K against 260K tokens) and work measured by AA Intelligence Index or Humanity's Last Exam (Artificial Analysis run), where it leads.
Which is cheaper, Mercury 2.5 or Qwen3.5-9B?
Mercury 2.5 costs $0.04 per million input tokens and $0.15 per million output tokens; Qwen3.5-9B costs $0.10 and $0.15. Cost per solved task is known for neither of them.
Which has the bigger context window, Mercury 2.5 or Qwen3.5-9B?
Qwen3.5-9B, with 262K tokens against 260K.
How many benchmarks is the Mercury 2.5 vs Qwen3.5-9B comparison based on?
4 benchmarks have a result for both models as of October 22, 2026. Mercury 2.5's coding index rests on 2 independent benchmarks and Qwen3.5-9B's on 0. Where only one model has a result, the page says so instead of naming a winner.