Mercury 2.5 vs Qwen3 Next 80B A3B Instruct
Mercury 2.5 leads Qwen3 Next 80B A3B Instruct on the Artificial Analysis Intelligence Index, 12.3 to 9.6, and Mercury 2.5 costs 7.3x less per output token ($0.15 vs $1.10 per million). Updated October 22, 2026; 3 benchmarks have a result for both.
Mercury 2.5 or Qwen3 Next 80B A3B Instruct: which to pick
Only Mercury 2.5 has a coding score, 1.6 on the Vibe Coding Index; Qwen3 Next 80B A3B Instruct has none, so the two cannot be ranked on coding.
Pick Qwen3 Next 80B A3B Instruct for a larger context window (262K against 260K tokens) and work measured by Humanity's Last Exam (Artificial Analysis run), where it leads.
- Shared results
- 3
- Mercury 2.5 only
- 19
- Qwen3 Next 80B A3B Instruct only
- 38
- Like-for-like categories
- 3 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
Mercury 2.5
Mercury 2.5 leads, 12.3 against 9.6.
Same board, both measured
Long documents
Listed context window
Qwen3 Next 80B A3B Instruct
Qwen3 Next 80B A3B Instruct takes 262K tokens against 260K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mercury 2.5
Mercury 2.5 $0.0001, Qwen3 Next 80B A3B Instruct $0.0006 per turn.
List prices
At a glance
- Coding index
- Mercury 2.5 1.6 from 2 benchmarks, Qwen3 Next 80B A3B Instruct not benchmarked.
- Widest category
- General, 8.0 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Mercury 2.5 71.7% against Qwen3 Next 80B A3B Instruct 63.7%, 8.0 points apart on a 0 to 100 scale.
- Output price
- Mercury 2.5 $0.15 against Qwen3 Next 80B A3B Instruct $1.10 per million output tokens, 7.3x apart.
- Context window
- Mercury 2.5 takes 260K tokens, Qwen3 Next 80B A3B Instruct 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 Next 80B A3B Instruct | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 0 | 1 | ||
| General | 1 | 0 | ||
| All shared boards | 2 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index13 boards, none shared
| Benchmark | Mercury 2.5 | Qwen3 Next 80B A3B Instruct | Leader |
|---|---|---|---|
| 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 |
| LiveCodeBench (Artificial Analysis run) | Not measured | 78.4% | Only Qwen3 Next 80B A3B Instruct measured |
| SciCode | 38.5% | Not measured | Only Mercury 2.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 9.8% | Only Qwen3 Next 80B A3B Instruct measured |
| ALE-Bench | 301.6 | Not measured | Only Mercury 2.5 measured |
| LMArena Text: coding | Not measured | 1440 | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text: software and IT | Not measured | 1447 | Only Qwen3 Next 80B A3B Instruct measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Mercury 2.5 | Qwen3 Next 80B A3B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.3 | 9.6 | Mercury 2.5 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Mercury 2.5 | Qwen3 Next 80B A3B Instruct | 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 | 41.5% | Only Qwen3 Next 80B A3B Instruct 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 Next 80B A3B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 75.9% | Only Qwen3 Next 80B A3B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | 11.8% | 12.6% | Qwen3 Next 80B A3B Instruct |
| LMArena Text: hard prompts | Not measured | 1428 | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text: expert | Not measured | 1417 | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text: hard prompts in English | Not measured | 1431 | Only Qwen3 Next 80B A3B Instruct measured |
| Kagi LLM Benchmark | Not measured | 66.7% | Only Qwen3 Next 80B A3B Instruct measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Mercury 2.5 | Qwen3 Next 80B A3B Instruct | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 84.3 | Only Qwen3 Next 80B A3B Instruct measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 84.3% | Only Qwen3 Next 80B A3B Instruct measured |
| ProofBench | 3.0% | Not measured | Only Mercury 2.5 measured |
| LMArena Text: math | Not measured | 1440 | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text: mathematical work | Not measured | 1434 | Only Qwen3 Next 80B A3B Instruct measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Mercury 2.5 | Qwen3 Next 80B A3B Instruct | 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 |
| MMLU Pro (Artificial Analysis run) | Not measured | 82.4% | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text: science | Not measured | 1434 | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text: medicine and healthcare | Not measured | 1455 | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text: legal and government | Not measured | 1431 | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text: business and finance | Not measured | 1422 | Only Qwen3 Next 80B A3B Instruct measured |
| Neither model has a result on the other 11 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mercury 2.5 | Qwen3 Next 80B A3B Instruct | 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 Next 80B A3B Instruct | 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 | 60.7% | Only Qwen3 Next 80B A3B Instruct measured |
| Long context reasoning (AA-LCR) | 71.7% | 63.7% | Mercury 2.5 |
| Fiction.LiveBench | Not measured | 55.6% | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text | Not measured | 1417 | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text: instruction following | Not measured | 1388 | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text: creative writing | Not measured | 1333 | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text: English | Not measured | 1421 | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text: non-English | Not measured | 1407 | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text: Chinese | Not measured | 1460 | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text: German | Not measured | 1416 | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text: Japanese | Not measured | 1395 | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text: Korean | Not measured | 1363 | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text: French | Not measured | 1414 | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text: Spanish | Not measured | 1435 | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text: Russian | Not measured | 1404 | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text: Polish | Not measured | 1398 | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text: multi-turn | Not measured | 1416 | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text: longer queries | Not measured | 1402 | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text: writing and language | Not measured | 1355 | Only Qwen3 Next 80B A3B Instruct measured |
| LMArena Text: entertainment and media | Not measured | 1349 | Only Qwen3 Next 80B A3B Instruct measured |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mercury 2.5 | Qwen3 Next 80B A3B Instruct | 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 Next 80B A3B Instruct |
|---|---|---|
| Maker | Inception | Qwen |
| Weights | No public weights listed | Open weights |
| Released | September 8, 2026 | September 11, 2025 |
| Context window | 260K | 262K |
| Input $/Mtok | $0.04 | $0.10 |
| Output $/Mtok | $0.15 | $1.10 |
| 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 Next 80B A3B Instruct for coding?
Only Mercury 2.5 has a coding score, 1.6 on the Vibe Coding Index; Qwen3 Next 80B A3B Instruct has none, so the two cannot be ranked on coding. Pick Qwen3 Next 80B A3B Instruct for a larger context window (262K against 260K tokens) and work measured by Humanity's Last Exam (Artificial Analysis run), where it leads.
Which is cheaper, Mercury 2.5 or Qwen3 Next 80B A3B Instruct?
Mercury 2.5 costs $0.04 per million input tokens and $0.15 per million output tokens; Qwen3 Next 80B A3B Instruct costs $0.10 and $1.10. Cost per solved task is known for neither of them.
Which has the bigger context window, Mercury 2.5 or Qwen3 Next 80B A3B Instruct?
Qwen3 Next 80B A3B Instruct, with 262K tokens against 260K.
How many benchmarks is the Mercury 2.5 vs Qwen3 Next 80B A3B Instruct comparison based on?
3 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 Next 80B A3B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.