Mercury 2 vs Qwen3 235B A22B Instruct 2507
Qwen3 235B A22B Instruct 2507 leads Mercury 2 on the Artificial Analysis Intelligence Index, 12.0 to 10.8, at about the same output price ($0.775 vs $0.75 per million). Updated October 22, 2026; 15 benchmarks have a result for both.
Mercury 2 or Qwen3 235B A22B Instruct 2507: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Mercury 2 for output tokens 1.0x cheaper ($0.75 vs $0.775 per million) and work measured by SciCode or AA Intelligence Index, where it leads.
- Shared results
- 15
- Mercury 2 only
- 27
- Qwen3 235B A22B Instruct 2507 only
- 9
- 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 235B A22B Instruct 2507
Qwen3 235B A22B Instruct 2507 leads, 12.0 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
Qwen3 235B A22B Instruct 2507
Qwen3 235B A22B Instruct 2507 leads, 1058 against 1035.
Same board, both measured
Long documents
Listed context window
Qwen3 235B A22B Instruct 2507
Qwen3 235B A22B Instruct 2507 takes 262K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 235B A22B Instruct 2507
Mercury 2 $0.0006, Qwen3 235B A22B Instruct 2507 $0.0005 per turn.
List prices
At a glance
- Coding index
- Mercury 2 not benchmarked, Qwen3 235B A22B Instruct 2507 not benchmarked.
- Widest category
- General, 23.5 points apart on average over 2 shared benchmarks.
- Widest single gap
- Long context reasoning (AA-LCR): Mercury 2 43.7% against Qwen3 235B A22B Instruct 2507 72.0%, 28.3 points apart on a 0 to 100 scale.
- Output price
- Mercury 2 $0.75 against Qwen3 235B A22B Instruct 2507 $0.775 per million output tokens, 1.0x apart.
- Context window
- Mercury 2 takes 128K tokens, Qwen3 235B A22B Instruct 2507 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 235B A22B Instruct 2507 | Ties |
|---|---|---|---|---|
| Coding | 2 | 1 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 1 | 1 | ||
| Web design | 2 | 4 | ||
| General | 1 | 1 | ||
| All shared boards | 7 | 8 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index21
| Benchmark | Mercury 2 | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| LMArena WebDev | 1171 | Not measured | Only Mercury 2 measured |
| AA Coding Index | 31.1 | Not measured | Only Mercury 2 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 78.8% | Only Qwen3 235B A22B Instruct 2507 measured |
| SciCode | 37.7% | 41.4% | Qwen3 235B A22B Instruct 2507 |
| Terminal-Bench Hard (Artificial Analysis run) | 26.5% | 15.2% | Mercury 2 |
| WeirdML | 43.2% | 38.7% | Mercury 2 |
| ALE-Bench | 785.6 | Not measured | Only Mercury 2 measured |
| LMArena Text: coding | 1390 | Not measured | Only Mercury 2 measured |
| LMArena Text: software and IT | 1385 | Not measured | Only Mercury 2 measured |
| LMArena WebDev: React | 1160 | Not measured | Only Mercury 2 measured |
| LMArena WebDev: HTML | 1199 | Not measured | Only Mercury 2 measured |
| Neither model has a result on the other 32 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mercury 2 | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| AA Intelligence Index | 10.8 | 12.0 | Qwen3 235B A22B Instruct 2507 |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Mercury 2 | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| AA Agentic Index | 1.6 | Not measured | Only Mercury 2 measured |
| τ²-Bench Telecom | 70.8% | 53.2% | Mercury 2 |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 52.1% | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Mercury 2 | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 77.0% | 79.0% | Qwen3 235B A22B Instruct 2507 |
| Humanity's Last Exam (Artificial Analysis run) | 17.1% | 15.9% | Mercury 2 |
| ARC-AGI | Not measured | 11.0% | Only Qwen3 235B A22B Instruct 2507 measured |
| ARC-AGI-2 | Not measured | 1.3% | Only Qwen3 235B A22B Instruct 2507 measured |
| LMArena Text: hard prompts | 1362 | Not measured | Only Mercury 2 measured |
| LMArena Text: expert | 1358 | Not measured | Only Mercury 2 measured |
| LMArena Text: hard prompts in English | 1374 | Not measured | Only Mercury 2 measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Mercury 2 | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 91.0 | Only Qwen3 235B A22B Instruct 2507 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 91.0% | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Mercury 2 | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 84.3% | Only Qwen3 235B A22B Instruct 2507 measured |
| LMArena Text: science | 1343 | Not measured | Only Mercury 2 measured |
| LMArena Text: medicine and healthcare | 1363 | Not measured | Only Mercury 2 measured |
| LMArena Text: business and finance | 1349 | Not measured | Only Mercury 2 measured |
| Neither model has a result on the other 16 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index24
| Benchmark | Mercury 2 | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| Design Arena: websites | 1035 | 1058 | Qwen3 235B A22B Instruct 2507 |
| Design Arena: UI components | 996 | 963 | Mercury 2 |
| Design Arena: game dev | 983 | 951 | Mercury 2 |
| Design Arena: data viz | 1010 | 1070 | Qwen3 235B A22B Instruct 2507 |
| Design Arena: 3D | 982 | 994 | Qwen3 235B A22B Instruct 2507 |
| Design Arena: all code categories | 1021 | 1038 | Qwen3 235B A22B Instruct 2507 |
| Design Arena: SVG | 997 | 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 index11
| Benchmark | Mercury 2 | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| IFBench | 69.8% | 51.2% | Mercury 2 |
| Long context reasoning (AA-LCR) | 43.7% | 72.0% | Qwen3 235B A22B Instruct 2507 |
| DTBench | Not measured | 78.4% | Only Qwen3 235B A22B Instruct 2507 measured |
| LMCA | Not measured | 23.5% | Only Qwen3 235B A22B Instruct 2507 measured |
| LMArena Text | 1354 | Not measured | Only Mercury 2 measured |
| LMArena Text: instruction following | 1328 | Not measured | Only Mercury 2 measured |
| LMArena Text: creative writing | 1286 | Not measured | Only Mercury 2 measured |
| LMArena Text: English | 1378 | Not measured | Only Mercury 2 measured |
| LMArena Text: non-English | 1330 | Not measured | Only Mercury 2 measured |
| LMArena Text: Chinese | 1414 | Not measured | Only Mercury 2 measured |
| LMArena Text: Russian | 1304 | Not measured | Only Mercury 2 measured |
| LMArena Text: multi-turn | 1357 | Not measured | Only Mercury 2 measured |
| LMArena Text: longer queries | 1329 | Not measured | Only Mercury 2 measured |
| LMArena Text: writing and language | 1300 | Not measured | Only Mercury 2 measured |
| LMArena Text: entertainment and media | 1314 | Not measured | Only Mercury 2 measured |
| Neither model has a result on the other 33 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mercury 2 | Qwen3 235B A22B Instruct 2507 | 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
Qwen3 235B A22B Instruct 2507 costs less
Repository review
50K input + 3K output tokens
Qwen3 235B A22B Instruct 2507 costs less
Agent loop
200K input + 10K output tokens
Qwen3 235B A22B Instruct 2507 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 235B A22B Instruct 2507 |
|---|---|---|
| Maker | Inception | Qwen |
| Weights | No public weights listed | Open weights |
| Released | March 4, 2026 | July 21, 2025 |
| Context window | 128K | 262K |
| Input $/Mtok | $0.25 | $0.145 |
| Output $/Mtok | $0.75 | $0.775 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mercury 2 better than Qwen3 235B A22B Instruct 2507 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Mercury 2 for output tokens 1.0x cheaper ($0.75 vs $0.775 per million) and work measured by SciCode or AA Intelligence Index, where it leads.
Which is cheaper, Mercury 2 or Qwen3 235B A22B Instruct 2507?
Mercury 2 costs $0.25 per million input tokens and $0.75 per million output tokens; Qwen3 235B A22B Instruct 2507 costs $0.145 and $0.775. Cost per solved task is known for neither of them.
Which has the bigger context window, Mercury 2 or Qwen3 235B A22B Instruct 2507?
Qwen3 235B A22B Instruct 2507, with 262K tokens against 128K.
How many benchmarks is the Mercury 2 vs Qwen3 235B A22B Instruct 2507 comparison based on?
15 benchmarks have a result for both models as of October 22, 2026. Mercury 2's coding index rests on 1 independent benchmark and Qwen3 235B A22B Instruct 2507's on 0. Where only one model has a result, the page says so instead of naming a winner.