Mercury 3 vs Qwen3 30B A3B Instruct 2507
Mercury 3 and Qwen3 30B A3B Instruct 2507 cost about the same per output token ($0.35 vs $0.30 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.35
- Coding
- No data
- Overall
- 41.5
- Output $/M
- $0.30
Mercury 3 or Qwen3 30B A3B Instruct 2507: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Mercury 3 for a larger context window (1M against 262K tokens).
No benchmark has tested both yet. Mercury 3 has results on Design Arena: websites, Design Arena: UI components and Design Arena: game dev; Qwen3 30B A3B Instruct 2507 on LiveCodeBench (Artificial Analysis run), SciCode and Terminal-Bench Hard (Artificial Analysis run). 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 30B A3B Instruct 2507 has a AA Intelligence Index result, 7.5.
Directional only
Web design
Design Arena: websites
No call
Only Mercury 3 has a Design Arena: websites result, 1229.
Directional only
Long documents
Listed context window
Mercury 3
Mercury 3 takes 1M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 30B A3B Instruct 2507
Mercury 3 $0.0003, Qwen3 30B A3B Instruct 2507 $0.0002 per turn.
List prices
At a glance
- Coding index
- Mercury 3 not benchmarked, Qwen3 30B A3B Instruct 2507 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
- Mercury 3 $0.35 against Qwen3 30B A3B Instruct 2507 $0.30 per million output tokens, 1.2x apart.
- Context window
- Mercury 3 takes 1M tokens, Qwen3 30B A3B Instruct 2507 262K.
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 Index5 boards measured
| Benchmark | Mercury 3 | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | Not measured | 70.7% | Only Qwen3 30B A3B Instruct 2507 measured |
| SciCode | Not measured | 33.0% | Only Qwen3 30B A3B Instruct 2507 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 6.1% | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: coding | Not measured | 1416 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: software and IT | Not measured | 1417 | Only Qwen3 30B A3B Instruct 2507 measured |
| Neither model has a result on the other 38 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Mercury 3 | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 7.5 | Only Qwen3 30B A3B Instruct 2507 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Mercury 3 | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 28.1% | Only Qwen3 30B A3B Instruct 2507 measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 41.4% | Only Qwen3 30B A3B Instruct 2507 measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Mercury 3 | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 70.7% | Only Qwen3 30B A3B Instruct 2507 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 10.3% | Only Qwen3 30B A3B Instruct 2507 measured |
| GPQA Diamond (Epoch AI run) | Not measured | 55.6% | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: hard prompts | Not measured | 1398 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: expert | Not measured | 1397 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: hard prompts in English | Not measured | 1406 | Only Qwen3 30B A3B Instruct 2507 measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | Mercury 3 | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 66.3 | Only Qwen3 30B A3B Instruct 2507 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 66.3% | Only Qwen3 30B A3B Instruct 2507 measured |
| OTIS Mock AIME | Not measured | 62.2% | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: math | Not measured | 1394 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: mathematical work | Not measured | 1411 | Only Qwen3 30B A3B Instruct 2507 measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | Mercury 3 | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 80.5% | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: science | Not measured | 1393 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: medicine and healthcare | Not measured | 1392 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: legal and government | Not measured | 1387 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: business and finance | Not measured | 1389 | Only Qwen3 30B A3B Instruct 2507 measured |
| Neither model has a result on the other 15 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | Mercury 3 | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| Design Arena: websites | 1229 | Not measured | Only Mercury 3 measured |
| Design Arena: UI components | 1247 | Not measured | Only Mercury 3 measured |
| Design Arena: game dev | 1225 | Not measured | Only Mercury 3 measured |
| Design Arena: data viz | 1205 | Not measured | Only Mercury 3 measured |
| Design Arena: all code categories | 1231 | Not measured | Only Mercury 3 measured |
| Neither model has a result on the other 4 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index21 boards measured
| Benchmark | Mercury 3 | Qwen3 30B A3B Instruct 2507 | Leader |
|---|---|---|---|
| IFBench | Not measured | 50.7% | Only Qwen3 30B A3B Instruct 2507 measured |
| Long context reasoning (AA-LCR) | Not measured | 61.3% | Only Qwen3 30B A3B Instruct 2507 measured |
| DTBench | Not measured | 67.2% | Only Qwen3 30B A3B Instruct 2507 measured |
| LMCA | Not measured | 22.4% | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text | Not measured | 1384 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: instruction following | Not measured | 1363 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: creative writing | Not measured | 1317 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: English | Not measured | 1391 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: non-English | Not measured | 1372 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: Chinese | Not measured | 1432 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: German | Not measured | 1379 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: Japanese | Not measured | 1337 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: Korean | Not measured | 1330 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: French | Not measured | 1419 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: Spanish | Not measured | 1404 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: Russian | Not measured | 1369 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: Polish | Not measured | 1353 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: multi-turn | Not measured | 1378 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: longer queries | Not measured | 1378 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: writing and language | Not measured | 1336 | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: entertainment and media | Not measured | 1334 | Only Qwen3 30B A3B Instruct 2507 measured |
| Neither model has a result on the other 27 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 30B A3B Instruct 2507 costs less
Repository review
50K input + 3K output tokens
Qwen3 30B A3B Instruct 2507 costs less
Agent loop
200K input + 10K output tokens
Qwen3 30B A3B 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 3 | Qwen3 30B A3B Instruct 2507 |
|---|---|---|
| Maker | Inception | Qwen |
| Weights | No public weights listed | Open weights |
| Released | October 16, 2026 | July 29, 2025 |
| Context window | 1M | 262K |
| Input $/Mtok | $0.10 | $0.09 |
| Output $/Mtok | $0.35 | $0.30 |
| 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 Mercury 3 better than Qwen3 30B A3B Instruct 2507 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Mercury 3 for a larger context window (1M against 262K tokens).
Which is cheaper, Mercury 3 or Qwen3 30B A3B Instruct 2507?
Mercury 3 costs $0.10 per million input tokens and $0.35 per million output tokens; Qwen3 30B A3B Instruct 2507 costs $0.09 and $0.30. Cost per solved task is known for neither of them.
Which has the bigger context window, Mercury 3 or Qwen3 30B A3B Instruct 2507?
Mercury 3, with 1M tokens against 262K.
Has any benchmark tested both Mercury 3 and Qwen3 30B A3B Instruct 2507?
No benchmark has tested both yet. Mercury 3 has results on Design Arena: websites, Design Arena: UI components and Design Arena: game dev; Qwen3 30B A3B Instruct 2507 on LiveCodeBench (Artificial Analysis run), SciCode and Terminal-Bench Hard (Artificial Analysis run). Compare them on price and context instead. Data as of October 22, 2026.