Qwen2.5 Coder 32B Instruct vs Qwen3.6 27B
Qwen3.6 27B leads Qwen2.5 Coder 32B Instruct on the Artificial Analysis Intelligence Index, 21.4 to 6.7, and Qwen2.5 Coder 32B Instruct costs 2.7x less per output token ($1.00 vs $2.70 per million). Updated October 22, 2026; 4 benchmarks have a result for both.
Qwen2.5 Coder 32B Instruct or Qwen3.6 27B: which to pick
Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; Qwen2.5 Coder 32B Instruct has none, so the two cannot be ranked on coding.
Pick Qwen2.5 Coder 32B Instruct for output tokens 2.7x cheaper ($1.00 vs $2.70 per million).
- Shared results
- 4
- Qwen2.5 Coder 32B Instruct only
- 31
- Qwen3.6 27B only
- 20
- Like-for-like categories
- 2 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 Qwen3.6 27B has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.6 27B
Qwen3.6 27B leads, 21.4 against 6.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.6 27B has a AA Agentic Index result, 18.5.
Directional only
Long documents
Listed context window
Qwen3.6 27B
Qwen3.6 27B takes 262K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 Coder 32B Instruct
Qwen2.5 Coder 32B Instruct $0.0012, Qwen3.6 27B $0.0018 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.6 27B has the number, $1.30.
Directional only
At a glance
- Coding index
- Qwen2.5 Coder 32B Instruct not benchmarked, Qwen3.6 27B 36.8 from 2 benchmarks.
- Widest category
- Reasoning, 31.1 points apart on average over 2 shared benchmarks.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): Qwen2.5 Coder 32B Instruct 41.7% against Qwen3.6 27B 84.2%, 42.5 points apart on a 0 to 100 scale.
- Output price
- Qwen2.5 Coder 32B Instruct $1.00 against Qwen3.6 27B $2.70 per million output tokens, 2.7x apart.
- Context window
- Qwen2.5 Coder 32B Instruct takes 33K tokens, Qwen3.6 27B 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 | Qwen2.5 Coder 32B Instruct | Bars | Qwen3.6 27B | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 2 | ||
| Reasoning | 0 | 2 | ||
| All shared boards | 0 | 4 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index10 boards, none shared
| Benchmark | Qwen2.5 Coder 32B Instruct | Qwen3.6 27B | Leader |
|---|---|---|---|
| Aider polyglot | 16.4% | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| SWE-bench Verified | 47.0% | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| AA Coding Index | Not measured | 53.7 | Only Qwen3.6 27B measured |
| SWE-bench Verified (Vals AI) | Not measured | 70.0% | Only Qwen3.6 27B measured |
| Vibe Code Bench (Vals AI) | Not measured | 11.9% | Only Qwen3.6 27B measured |
| LiveCodeBench (Artificial Analysis run) | 29.5% | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| SciCode | Not measured | 42.8% | Only Qwen3.6 27B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 34.8% | Only Qwen3.6 27B measured |
| LMArena Text: coding | 1276 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: software and IT | 1261 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Qwen2.5 Coder 32B Instruct | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.7 | 21.4 | Qwen3.6 27B |
| Epoch Capabilities Index | 119.5 | 146.2 | Qwen3.6 27B |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Qwen2.5 Coder 32B Instruct | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 18.5 | Only Qwen3.6 27B measured |
| τ²-Bench Telecom | Not measured | 94.2% | Only Qwen3.6 27B 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 | Qwen2.5 Coder 32B Instruct | Qwen3.6 27B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 41.7% | 84.2% | Qwen3.6 27B |
| Humanity's Last Exam (Artificial Analysis run) | 3.5% | 23.1% | Qwen3.6 27B |
| GPQA Diamond (Epoch AI run) | Not measured | 85.9% | Only Qwen3.6 27B measured |
| Mystery game puzzles | Not measured | 7.0% | Only Qwen3.6 27B measured |
| LMArena Text: hard prompts | 1251 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: expert | 1221 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: hard prompts in English | 1269 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Qwen2.5 Coder 32B Instruct | Qwen3.6 27B | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 91.1% | Only Qwen3.6 27B measured |
| FrontierMath, tiers 1 to 3 | Not measured | 35.1% | Only Qwen3.6 27B measured |
| GSM8K | 93.0% | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: math | 1251 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: mathematical work | 1243 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | Qwen2.5 Coder 32B Instruct | Qwen3.6 27B | Leader |
|---|---|---|---|
| CaseLaw (Vals AI) | Not measured | 53.2% | Only Qwen3.6 27B measured |
| CorpFin (Vals AI) | Not measured | 62.3% | Only Qwen3.6 27B measured |
| TaxEval (Vals AI) | Not measured | 71.3% | Only Qwen3.6 27B measured |
| MortgageTax (Vals AI) | Not measured | 68.3% | Only Qwen3.6 27B measured |
| MMLU Pro (Artificial Analysis run) | 63.5% | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| MMLU | 79.1% | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| AI2 Reasoning Challenge | 70.5% | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: science | 1221 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: medicine and healthcare | 1187 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: legal and government | 1257 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: business and finance | 1224 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| Neither model has a result on the other 7 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen2.5 Coder 32B Instruct | Qwen3.6 27B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index18 boards, none shared
| Benchmark | Qwen2.5 Coder 32B Instruct | Qwen3.6 27B | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 45.6% | Only Qwen3.6 27B measured |
| IFBench | Not measured | 67.6% | Only Qwen3.6 27B measured |
| Long context reasoning (AA-LCR) | Not measured | 77.3% | Only Qwen3.6 27B measured |
| DTBench | Not measured | 78.1% | Only Qwen3.6 27B measured |
| LMCA | Not measured | 34.5% | Only Qwen3.6 27B measured |
| HellaSwag | 83.0% | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| WinoGrande | 80.8% | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text | 1230 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: instruction following | 1223 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: creative writing | 1174 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: English | 1248 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: non-English | 1205 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: Chinese | 1222 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: Russian | 1228 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: multi-turn | 1222 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: longer queries | 1251 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: writing and language | 1191 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: entertainment and media | 1150 | Not measured | Only Qwen2.5 Coder 32B Instruct measured |
| Neither model has a result on the other 28 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen2.5 Coder 32B Instruct | Qwen3.6 27B | 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
Qwen2.5 Coder 32B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen3.6 27B costs less
Agent loop
200K input + 10K output tokens
Qwen3.6 27B 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 | Qwen2.5 Coder 32B Instruct | Qwen3.6 27B |
|---|---|---|
| Maker | Qwen | Qwen |
| Weights | Open weights | Open weights |
| Released | November 11, 2024 | April 27, 2026 |
| Context window | 33K | 262K |
| Input $/Mtok | $0.66 | $0.45 |
| Output $/Mtok | $1.00 | $2.70 |
| Coding index | Not benchmarked | 36.8 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | Not computable | $1.30 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Qwen2.5 Coder 32B Instruct better than Qwen3.6 27B for coding?
Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; Qwen2.5 Coder 32B Instruct has none, so the two cannot be ranked on coding. Pick Qwen2.5 Coder 32B Instruct for output tokens 2.7x cheaper ($1.00 vs $2.70 per million).
Which is cheaper, Qwen2.5 Coder 32B Instruct or Qwen3.6 27B?
Qwen2.5 Coder 32B Instruct costs $0.66 per million input tokens and $1.00 per million output tokens; Qwen3.6 27B costs $0.45 and $2.70. Cost per solved task is known for Qwen3.6 27B only.
Which has the bigger context window, Qwen2.5 Coder 32B Instruct or Qwen3.6 27B?
Qwen3.6 27B, with 262K tokens against 33K.
How many benchmarks is the Qwen2.5 Coder 32B Instruct vs Qwen3.6 27B comparison based on?
4 benchmarks have a result for both models as of October 22, 2026. Qwen2.5 Coder 32B Instruct's coding index rests on 0 independent benchmarks and Qwen3.6 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.