Mercury 2 vs Qwen3.5-27B
Qwen3.5-27B leads Mercury 2 on the Artificial Analysis Intelligence Index, 22.9 to 10.8, and Mercury 2 costs 2.1x less per output token ($0.75 vs $1.56 per million). Updated October 22, 2026; 31 benchmarks have a result for both.
Mercury 2 or Qwen3.5-27B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Mercury 2 for output tokens 2.1x cheaper ($0.75 vs $1.56 per million) and work measured by LMArena WebDev or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 31
- Mercury 2 only
- 11
- Qwen3.5-27B only
- 22
- Like-for-like categories
- 6 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.5-27B
Qwen3.5-27B leads, 22.9 against 10.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mercury 2 has a AA Agentic Index result, 1.6.
Directional only
Web design
Design Arena: websites
No call
Only Mercury 2 has a Design Arena: websites result, 1036.
Directional only
Long documents
Listed context window
Qwen3.5-27B
Qwen3.5-27B takes 262K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mercury 2
Mercury 2 $0.0006, Qwen3.5-27B $0.0010 per turn.
List prices
At a glance
- Coding index
- Mercury 2 not benchmarked, Qwen3.5-27B not benchmarked.
- Widest category
- Agentic work, 23.1 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Mercury 2 43.7% against Qwen3.5-27B 77.7%, 34.0 points apart on a 0 to 100 scale.
- Output price
- Mercury 2 $0.75 against Qwen3.5-27B $1.56 per million output tokens, 2.1x apart.
- Context window
- Mercury 2 takes 128K tokens, Qwen3.5-27B 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 | Bars | Qwen3.5-27B | Ties |
|---|---|---|---|---|
| Coding | 2 | 6 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Knowledge | 0 | 3 | ||
| General | 0 | 13 | ||
| All shared boards | 2 | 29 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index26
| Benchmark | Mercury 2 | Qwen3.5-27B | Leader |
|---|---|---|---|
| LMArena WebDev | 1171 | 1358 | Qwen3.5-27B |
| AA Coding Index | 31.1 | Not measured | Only Mercury 2 measured |
| SciCode | 37.7% | Not measured | Only Mercury 2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 26.5% | 32.6% | Qwen3.5-27B |
| WeirdML | 43.2% | 39.5% | Mercury 2 |
| ALE-Bench | 785.6 | 349.4 | Mercury 2 |
| LMArena Text: coding | 1390 | 1427 | Qwen3.5-27B |
| LMArena Text: software and IT | 1385 | 1431 | Qwen3.5-27B |
| LMArena WebDev: React | 1160 | 1346 | Qwen3.5-27B |
| LMArena WebDev: HTML | 1199 | 1386 | Qwen3.5-27B |
| Neither model has a result on the other 29 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mercury 2 | Qwen3.5-27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 10.8 | 22.9 | Qwen3.5-27B |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mercury 2 | Qwen3.5-27B | Leader |
|---|---|---|---|
| AA Agentic Index | 1.6 | Not measured | Only Mercury 2 measured |
| τ²-Bench Telecom | 70.8% | 93.9% | Qwen3.5-27B |
| Vending-Bench 2 | Not measured | $202 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Mercury 2 | Qwen3.5-27B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 77.0% | 85.8% | Qwen3.5-27B |
| Humanity's Last Exam (Artificial Analysis run) | 17.1% | 23.9% | Qwen3.5-27B |
| LMArena Text: hard prompts | 1362 | 1415 | Qwen3.5-27B |
| LMArena Text: expert | 1358 | 1427 | Qwen3.5-27B |
| LMArena Text: hard prompts in English | 1374 | 1429 | Qwen3.5-27B |
| LMArena Vision: diagrams | Not measured | 1249 | Only Qwen3.5-27B measured |
| LMArena Vision: homework | Not measured | 1256 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Mercury 2 | Qwen3.5-27B | Leader |
|---|---|---|---|
| LMArena Text: math | Not measured | 1429 | Only Qwen3.5-27B measured |
| LMArena Text: mathematical work | Not measured | 1429 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Mercury 2 | Qwen3.5-27B | Leader |
|---|---|---|---|
| LMArena Text: science | 1343 | 1437 | Qwen3.5-27B |
| LMArena Text: medicine and healthcare | 1363 | 1425 | Qwen3.5-27B |
| LMArena Text: legal and government | Not measured | 1405 | Only Qwen3.5-27B measured |
| LMArena Text: business and finance | 1349 | 1399 | Qwen3.5-27B |
| Neither model has a result on the other 14 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Mercury 2 | Qwen3.5-27B | Leader |
|---|---|---|---|
| Design Arena: websites | 1036 | Not measured | Only Mercury 2 measured |
| Design Arena: UI components | 996 | Not measured | Only Mercury 2 measured |
| Design Arena: game dev | 984 | Not measured | Only Mercury 2 measured |
| Design Arena: data viz | 1009 | Not measured | Only Mercury 2 measured |
| Design Arena: 3D | 982 | Not measured | Only Mercury 2 measured |
| Design Arena: all code categories | 1021 | Not measured | Only Mercury 2 measured |
| Design Arena: SVG | 998 | Not measured | Only Mercury 2 measured |
| Design Arena: ASCII art | 988 | Not measured | Only Mercury 2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index013
| Benchmark | Mercury 2 | Qwen3.5-27B | Leader |
|---|---|---|---|
| IFBench | 69.8% | 75.6% | Qwen3.5-27B |
| Long context reasoning (AA-LCR) | 43.7% | 77.7% | Qwen3.5-27B |
| DTBench | Not measured | 82.4% | Only Qwen3.5-27B measured |
| LMCA | Not measured | 34.0% | Only Qwen3.5-27B measured |
| LMArena Text | 1354 | 1409 | Qwen3.5-27B |
| LMArena Text: instruction following | 1328 | 1393 | Qwen3.5-27B |
| LMArena Text: creative writing | 1286 | 1362 | Qwen3.5-27B |
| LMArena Vision | Not measured | 1241 | Only Qwen3.5-27B measured |
| LMArena Text: English | 1378 | 1425 | Qwen3.5-27B |
| LMArena Text: non-English | 1330 | 1390 | Qwen3.5-27B |
| LMArena Text: Chinese | 1414 | 1478 | Qwen3.5-27B |
| LMArena Text: German | Not measured | 1392 | Only Qwen3.5-27B measured |
| LMArena Text: Japanese | Not measured | 1346 | Only Qwen3.5-27B measured |
| LMArena Text: Korean | Not measured | 1359 | Only Qwen3.5-27B measured |
| LMArena Text: French | Not measured | 1410 | Only Qwen3.5-27B measured |
| LMArena Text: Spanish | Not measured | 1407 | Only Qwen3.5-27B measured |
| LMArena Text: Russian | 1304 | 1390 | Qwen3.5-27B |
| LMArena Text: Polish | Not measured | 1375 | Only Qwen3.5-27B measured |
| LMArena Text: multi-turn | 1357 | 1410 | Qwen3.5-27B |
| LMArena Text: longer queries | 1329 | 1413 | Qwen3.5-27B |
| LMArena Text: writing and language | 1300 | 1380 | Qwen3.5-27B |
| LMArena Text: entertainment and media | 1314 | 1363 | Qwen3.5-27B |
| LMArena Vision: English | Not measured | 1246 | Only Qwen3.5-27B measured |
| LMArena Vision: Chinese | Not measured | 1301 | Only Qwen3.5-27B measured |
| LMArena Vision: reading text | Not measured | 1246 | Only Qwen3.5-27B measured |
| LMArena Vision: humor | Not measured | 1227 | Only Qwen3.5-27B measured |
| LMArena Vision: captioning | Not measured | 1170 | Only Qwen3.5-27B measured |
| LMArena Vision: recognizing things | Not measured | 1210 | Only Qwen3.5-27B measured |
| LMArena Vision: creative writing | Not measured | 1224 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 17 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mercury 2 | Qwen3.5-27B | 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 costs less
Repository review
50K input + 3K output tokens
Qwen3.5-27B costs less
Agent loop
200K input + 10K output tokens
Qwen3.5-27B 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 | Qwen3.5-27B |
|---|---|---|
| Maker | Inception | Qwen |
| Weights | No public weights listed | Open weights |
| Released | March 4, 2026 | February 25, 2026 |
| Context window | 128K | 262K |
| Input $/Mtok | $0.25 | $0.195 |
| Output $/Mtok | $0.75 | $1.56 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mercury 2 better than Qwen3.5-27B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Mercury 2 for output tokens 2.1x cheaper ($0.75 vs $1.56 per million) and work measured by LMArena WebDev or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, Mercury 2 or Qwen3.5-27B?
Mercury 2 costs $0.25 per million input tokens and $0.75 per million output tokens; Qwen3.5-27B costs $0.195 and $1.56. Cost per solved task is known for neither of them.
Which has the bigger context window, Mercury 2 or Qwen3.5-27B?
Qwen3.5-27B, with 262K tokens against 128K.
How many benchmarks is the Mercury 2 vs Qwen3.5-27B comparison based on?
31 benchmarks have a result for both models as of October 22, 2026. Mercury 2's coding index rests on 1 independent benchmark and Qwen3.5-27B's on 1. Where only one model has a result, the page says so instead of naming a winner.