LongCat 2.0 vs Qwen3.5-122B-A10B
LongCat 2.0 leads Qwen3.5-122B-A10B on the Artificial Analysis Intelligence Index, 19.1 to 17.7, and LongCat 2.0 costs 1.7x less per output token ($1.20 vs $2.08 per million). Updated October 23, 2026; 6 benchmarks have a result for both.
LongCat 2.0 or Qwen3.5-122B-A10B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick LongCat 2.0 for output tokens 1.7x cheaper ($1.20 vs $2.08 per million) and a larger context window (1.05M against 262K tokens).
- Shared results
- 6
- LongCat 2.0 only
- 2
- Qwen3.5-122B-A10B only
- 45
- 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
LongCat 2.0
LongCat 2.0 leads, 19.1 against 17.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only LongCat 2.0 has a AA Agentic Index result, 14.0.
Directional only
Long documents
Listed context window
LongCat 2.0
LongCat 2.0 takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
LongCat 2.0
LongCat 2.0 $0.0009, Qwen3.5-122B-A10B $0.0013 per turn.
List prices
At a glance
- Coding index
- LongCat 2.0 not benchmarked, Qwen3.5-122B-A10B not benchmarked.
- Widest category
- General, 11.3 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): LongCat 2.0 65.0% against Qwen3.5-122B-A10B 76.3%, 11.3 points apart on a 0 to 100 scale.
- Output price
- LongCat 2.0 $1.20 against Qwen3.5-122B-A10B $2.08 per million output tokens, 1.7x apart.
- Context window
- LongCat 2.0 takes 1.05M tokens, Qwen3.5-122B-A10B 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 | LongCat 2.0 | Bars | Qwen3.5-122B-A10B | Ties |
|---|---|---|---|---|
| Coding | 1 | 1 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 1 | 1 | ||
| General | 0 | 1 | ||
| All shared boards | 3 | 3 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index11
| Benchmark | LongCat 2.0 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1361 | Only Qwen3.5-122B-A10B measured |
| AA Coding Index | 45.3 | 43.3 | LongCat 2.0 |
| SciCode | 36.3% | 39.7% | Qwen3.5-122B-A10B |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 31.1% | Only Qwen3.5-122B-A10B measured |
| ALE-Bench | 793.5 | Not measured | Only LongCat 2.0 measured |
| LMArena Text: coding | Not measured | 1436 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: software and IT | Not measured | 1436 | Only Qwen3.5-122B-A10B measured |
| LMArena WebDev: React | Not measured | 1352 | Only Qwen3.5-122B-A10B measured |
| LMArena WebDev: HTML | Not measured | 1352 | Only Qwen3.5-122B-A10B measured |
| Neither model has a result on the other 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | LongCat 2.0 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| AA Intelligence Index | 19.1 | 17.7 | LongCat 2.0 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | LongCat 2.0 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| AA Agentic Index | 14.0 | Not measured | Only LongCat 2.0 measured |
| τ²-Bench Telecom | Not measured | 93.6% | Only Qwen3.5-122B-A10B measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | LongCat 2.0 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 78.0% | 85.7% | Qwen3.5-122B-A10B |
| Humanity's Last Exam (Artificial Analysis run) | 33.7% | 25.2% | LongCat 2.0 |
| Mystery game puzzles | Not measured | 17.0% | Only Qwen3.5-122B-A10B measured |
| LMArena Text: hard prompts | Not measured | 1421 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: expert | Not measured | 1431 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: hard prompts in English | Not measured | 1431 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: diagrams | Not measured | 1249 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: homework | Not measured | 1261 | Only Qwen3.5-122B-A10B 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 | LongCat 2.0 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| LMArena Text: math | Not measured | 1431 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: mathematical work | Not measured | 1431 | Only Qwen3.5-122B-A10B 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 | LongCat 2.0 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| LMArena Text: science | Not measured | 1433 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: medicine and healthcare | Not measured | 1423 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: legal and government | Not measured | 1422 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: business and finance | Not measured | 1414 | Only Qwen3.5-122B-A10B 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 | LongCat 2.0 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | LongCat 2.0 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| IFBench | Not measured | 75.7% | Only Qwen3.5-122B-A10B measured |
| Long context reasoning (AA-LCR) | 65.0% | 76.3% | Qwen3.5-122B-A10B |
| DTBench | Not measured | 84.3% | Only Qwen3.5-122B-A10B measured |
| LMCA | Not measured | 32.2% | Only Qwen3.5-122B-A10B measured |
| LMArena Text | Not measured | 1418 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: instruction following | Not measured | 1400 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: creative writing | Not measured | 1368 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision | Not measured | 1244 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: English | Not measured | 1428 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: non-English | Not measured | 1401 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: Chinese | Not measured | 1463 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: German | Not measured | 1425 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: Japanese | Not measured | 1367 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: Korean | Not measured | 1353 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: French | Not measured | 1443 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: Spanish | Not measured | 1425 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: Russian | Not measured | 1400 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: Polish | Not measured | 1399 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: multi-turn | Not measured | 1417 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: longer queries | Not measured | 1411 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: writing and language | Not measured | 1387 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: entertainment and media | Not measured | 1366 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: English | Not measured | 1249 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: Chinese | Not measured | 1301 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: reading text | Not measured | 1251 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: humor | Not measured | 1243 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: creative writing | Not measured | 1220 | Only Qwen3.5-122B-A10B measured |
| Neither model has a result on the other 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | LongCat 2.0 | Qwen3.5-122B-A10B | 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
LongCat 2.0 costs less
Repository review
50K input + 3K output tokens
LongCat 2.0 costs less
Agent loop
200K input + 10K output tokens
LongCat 2.0 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 | Meituan LongCat 2.0 | Qwen3.5-122B-A10B |
|---|---|---|
| Maker | Meituan | Qwen |
| Weights | Open weights | Open weights |
| Released | July 20, 2026 | February 25, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $0.30 | $0.26 |
| Output $/Mtok | $1.20 | $2.08 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is LongCat 2.0 better than Qwen3.5-122B-A10B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick LongCat 2.0 for output tokens 1.7x cheaper ($1.20 vs $2.08 per million) and a larger context window (1.05M against 262K tokens).
Which is cheaper, LongCat 2.0 or Qwen3.5-122B-A10B?
LongCat 2.0 costs $0.30 per million input tokens and $1.20 per million output tokens; Qwen3.5-122B-A10B costs $0.26 and $2.08. Cost per solved task is known for neither of them.
Which has the bigger context window, LongCat 2.0 or Qwen3.5-122B-A10B?
LongCat 2.0, with 1.05M tokens against 262K.
How many benchmarks is the LongCat 2.0 vs Qwen3.5-122B-A10B comparison based on?
6 benchmarks have a result for both models as of October 23, 2026. LongCat 2.0's coding index rests on 0 independent benchmarks and Qwen3.5-122B-A10B's on 1. Where only one model has a result, the page says so instead of naming a winner.