Qwen3.6 35B A3B vs Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B leads Qwen3.6 35B A3B on the Artificial Analysis Intelligence Index, 39.9 to 18.2, and Qwen3.6 35B A3B costs 6.0x less per output token ($1.00 vs $6.00 per million). Updated October 22, 2026; 7 benchmarks have a result for both.
Qwen3.6 35B A3B or Qwen3.8 2.4T A95B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Qwen3.6 35B A3B for output tokens 6.0x cheaper ($1.00 vs $6.00 per million) and work measured by AA Coding Index or SciCode, where it leads.
- Shared results
- 7
- Qwen3.6 35B A3B only
- 13
- Qwen3.8 2.4T A95B only
- 2
- Like-for-like categories
- 5 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
Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B leads, 39.9 against 18.2.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B leads, 50.1 against 13.1.
Same board, both measured
Long documents
Listed context window
Qwen3.8 2.4T A95B
Qwen3.8 2.4T A95B takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.6 35B A3B
Qwen3.6 35B A3B $0.0006, Qwen3.8 2.4T A95B $0.0050 per turn.
List prices
At a glance
- Coding index
- Qwen3.6 35B A3B not benchmarked, Qwen3.8 2.4T A95B not benchmarked.
- Widest category
- Agentic work, 37.0 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Agentic Index: Qwen3.6 35B A3B 13.1 against Qwen3.8 2.4T A95B 50.1, 37.0 points apart on a 0 to 100 scale.
- Output price
- Qwen3.6 35B A3B $1.00 against Qwen3.8 2.4T A95B $6.00 per million output tokens, 6.0x apart.
- Context window
- Qwen3.6 35B A3B takes 262K tokens, Qwen3.8 2.4T A95B 1.05M.
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 | Qwen3.6 35B A3B | Bars | Qwen3.8 2.4T A95B | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 7 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Qwen3.6 35B A3B | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| AA Coding Index | 41.9 | 71.9 | Qwen3.8 2.4T A95B |
| SciCode | 36.6% | 54.1% | Qwen3.8 2.4T A95B |
| Terminal-Bench Hard (Artificial Analysis run) | 34.8% | Not measured | Only Qwen3.6 35B A3B measured |
| Terminal-Bench | 24.6% | Not measured | Only Qwen3.6 35B A3B measured |
| WeirdML | 34.5% | Not measured | Only Qwen3.6 35B A3B measured |
| Neither model has a result on the other 38 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3.6 35B A3B | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| AA Intelligence Index | 18.2 | 39.9 | Qwen3.8 2.4T A95B |
| Epoch Capabilities Index | 143.7 | Not measured | Only Qwen3.6 35B A3B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3.6 35B A3B | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| AA Agentic Index | 13.1 | 50.1 | Qwen3.8 2.4T A95B |
| τ²-Bench Telecom | 95.3% | Not measured | Only Qwen3.6 35B A3B measured |
| MCP Atlas | Not measured | 84.5% | Only Qwen3.8 2.4T A95B measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Qwen3.6 35B A3B | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 84.1% | 93.5% | Qwen3.8 2.4T A95B |
| Humanity's Last Exam (Artificial Analysis run) | 22.2% | 42.4% | Qwen3.8 2.4T A95B |
| GPQA Diamond (Epoch AI run) | 84.8% | Not measured | Only Qwen3.6 35B A3B measured |
| Mystery game puzzles | 22.0% | Not measured | Only Qwen3.6 35B A3B measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Qwen3.6 35B A3B | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| OTIS Mock AIME | 86.7% | Not measured | Only Qwen3.6 35B A3B measured |
| FrontierMath, tiers 1 to 3 | 20.4% | Not measured | Only Qwen3.6 35B A3B measured |
| MathArena | Not measured | 31.6% | Only Qwen3.8 2.4T A95B measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3.6 35B A3B | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| Neither model has a result on 20 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3.6 35B A3B | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3.6 35B A3B | Qwen3.8 2.4T A95B | Leader |
|---|---|---|---|
| IFBench | 64.4% | Not measured | Only Qwen3.6 35B A3B measured |
| Long context reasoning (AA-LCR) | 71.7% | 80.3% | Qwen3.8 2.4T A95B |
| DTBench | 73.9% | Not measured | Only Qwen3.6 35B A3B measured |
| LMCA | 29.7% | Not measured | Only Qwen3.6 35B A3B measured |
| Surface Evolver Bench | 44.4% | Not measured | Only Qwen3.6 35B A3B measured |
| Neither model has a result on the other 43 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3.6 35B A3B | Qwen3.8 2.4T A95B | 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
Qwen3.6 35B A3B costs less
Repository review
50K input + 3K output tokens
Qwen3.6 35B A3B costs less
Agent loop
200K input + 10K output tokens
Qwen3.6 35B A3B 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 | Qwen3.6 35B A3B | Qwen3.8 2.4T A95B |
|---|---|---|
| Maker | Qwen | Qwen |
| Weights | Open weights | Open weights |
| Released | April 27, 2026 | August 12, 2026 |
| Context window | 262K | 1.05M |
| Input $/Mtok | $0.10 | $2.00 |
| Output $/Mtok | $1.00 | $6.00 |
| 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 Qwen3.6 35B A3B better than Qwen3.8 2.4T A95B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Qwen3.6 35B A3B for output tokens 6.0x cheaper ($1.00 vs $6.00 per million) and work measured by AA Coding Index or SciCode, where it leads.
Which is cheaper, Qwen3.6 35B A3B or Qwen3.8 2.4T A95B?
Qwen3.6 35B A3B costs $0.10 per million input tokens and $1.00 per million output tokens; Qwen3.8 2.4T A95B costs $2.00 and $6.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Qwen3.6 35B A3B or Qwen3.8 2.4T A95B?
Qwen3.8 2.4T A95B, with 1.05M tokens against 262K.
How many benchmarks is the Qwen3.6 35B A3B vs Qwen3.8 2.4T A95B comparison based on?
7 benchmarks have a result for both models as of October 22, 2026. Qwen3.6 35B A3B's coding index rests on 0 independent benchmarks and Qwen3.8 2.4T A95B's on 0. Where only one model has a result, the page says so instead of naming a winner.