Gemini 2.5 Pro vs Qwen3.6 27B
Qwen3.6 27B leads Gemini 2.5 Pro on coding, 36.8 to 19.1 on the Vibe Coding Index, and Qwen3.6 27B costs 3.7x less per output token ($2.70 vs $10.00 per million). Updated October 22, 2026; 22 benchmarks have a result for both.
Gemini 2.5 Pro or Qwen3.6 27B: which to pick
Pick Qwen3.6 27B for coding: it scores 36.8 on the Vibe Coding Index against 19.1 for Gemini 2.5 Pro, though on 2 benchmarks against 3.
Pick Gemini 2.5 Pro for a lower cost per solved task ($0.64 against $1.30) and a larger context window (1.05M against 262K tokens).
- Shared results
- 22
- Gemini 2.5 Pro only
- 68
- Qwen3.6 27B only
- 2
- Like-for-like categories
- 7 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
Qwen3.6 27B
Qwen3.6 27B scores 36.8 against 19.1.
3 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
Qwen3.6 27B
Qwen3.6 27B leads, 21.4 against 16.1.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3.6 27B
Qwen3.6 27B leads, 18.5 against 1.6.
Same board, both measured
Web design
Design Arena: websites
No call
Only Gemini 2.5 Pro has a Design Arena: websites result, 1167.
Directional only
Long documents
Listed context window
Gemini 2.5 Pro
Gemini 2.5 Pro takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.6 27B
Gemini 2.5 Pro $0.0063, Qwen3.6 27B $0.0018 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Gemini 2.5 Pro
Gemini 2.5 Pro $0.64, Qwen3.6 27B $1.30.
Published attempt cost and pass rate
At a glance
- Coding index
- Gemini 2.5 Pro 19.1 from 3 benchmarks, Qwen3.6 27B 36.8 from 2 benchmarks.
- Widest category
- Agentic work, 28.5 points apart on average over 2 shared benchmarks.
- Widest single gap
- τ²-Bench Telecom: Gemini 2.5 Pro 54.1% against Qwen3.6 27B 94.2%, 40.1 points apart on a 0 to 100 scale.
- Output price
- Gemini 2.5 Pro $10.00 against Qwen3.6 27B $2.70 per million output tokens, 3.7x apart.
- Context window
- Gemini 2.5 Pro takes 1.05M 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 | Gemini 2.5 Pro | Bars | Qwen3.6 27B | Ties |
|---|---|---|---|---|
| Coding | 1 | 4 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 2 | ||
| Reasoning | 1 | 2 | ||
| Math | 0 | 2 | ||
| Knowledge | 2 | 1 | ||
| General | 2 | 3 | ||
| All shared boards | 6 | 16 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index14
| Benchmark | Gemini 2.5 Pro | Qwen3.6 27B | Leader |
|---|---|---|---|
| SWE-bench Verified | 53.6% | Not measured | Only Gemini 2.5 Pro measured |
| LMArena WebDev | 1226 | Not measured | Only Gemini 2.5 Pro measured |
| AA Coding Index | 33.3 | 53.7 | Qwen3.6 27B |
| SWE-bench Verified (Vals AI) | 54.4% | 70.0% | Qwen3.6 27B |
| Vibe Code Bench (Vals AI) | 0.4% | 11.9% | Qwen3.6 27B |
| LiveCodeBench (Artificial Analysis run) | 80.1% | Not measured | Only Gemini 2.5 Pro measured |
| SciCode | 46.3% | 42.8% | Gemini 2.5 Pro |
| Terminal-Bench Hard (Artificial Analysis run) | 26.5% | 34.8% | Qwen3.6 27B |
| Terminal-Bench | 32.6% | Not measured | Only Gemini 2.5 Pro measured |
| GSO-Bench | 3.9% | Not measured | Only Gemini 2.5 Pro measured |
| WeirdML | 54.0% | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: coding | 1452 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: software and IT | 1459 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena WebDev: HTML | 1235 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena WebDev: from an image | 1285 | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Gemini 2.5 Pro | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.1 | 21.4 | Qwen3.6 27B |
| Epoch Capabilities Index | 145.1 | 146.2 | Qwen3.6 27B |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Gemini 2.5 Pro | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Agentic Index | 1.6 | 18.5 | Qwen3.6 27B |
| τ²-Bench Telecom | 54.1% | 94.2% | Qwen3.6 27B |
| GDPval | 23.3% | Not measured | Only Gemini 2.5 Pro measured |
| DeepResearch Bench | 41.5% | Not measured | Only Gemini 2.5 Pro measured |
| Vending-Bench 2 | $574 | Not measured | Only Gemini 2.5 Pro measured |
| BrowseComp-Plus | 29.5% | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 35 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index12
| Benchmark | Gemini 2.5 Pro | Qwen3.6 27B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 84.4% | 84.2% | Gemini 2.5 Pro |
| Humanity's Last Exam (Artificial Analysis run) | 22.5% | 23.1% | Qwen3.6 27B |
| GPQA Diamond (Epoch AI run) | 85.3% | 85.9% | Qwen3.6 27B |
| ARC-AGI | 41.0% | Not measured | Only Gemini 2.5 Pro measured |
| ARC-AGI-2 | 4.9% | Not measured | Only Gemini 2.5 Pro measured |
| Mystery game puzzles | Not measured | 7.0% | Only Qwen3.6 27B measured |
| LMArena Text: hard prompts | 1455 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: expert | 1452 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: hard prompts in English | 1453 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: diagrams | 1274 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: homework | 1281 | Not measured | Only Gemini 2.5 Pro measured |
| Kagi LLM Benchmark | 70.3% | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Gemini 2.5 Pro | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Math Index | 87.7 | Not measured | Only Gemini 2.5 Pro measured |
| AIME 2025 (Artificial Analysis run) | 87.7% | Not measured | Only Gemini 2.5 Pro measured |
| OTIS Mock AIME | 84.7% | 91.1% | Qwen3.6 27B |
| FrontierMath, tiers 1 to 3 | 24.6% | 35.1% | Qwen3.6 27B |
| FrontierMath, tier 4 | 0.0% | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: math | 1450 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: mathematical work | 1454 | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | Gemini 2.5 Pro | Qwen3.6 27B | Leader |
|---|---|---|---|
| CaseLaw (Vals AI) | 63.9% | 53.2% | Gemini 2.5 Pro |
| CorpFin (Vals AI) | 60.8% | 62.3% | Qwen3.6 27B |
| TaxEval (Vals AI) | Not measured | 71.3% | Only Qwen3.6 27B measured |
| MortgageTax (Vals AI) | 68.9% | 68.3% | Gemini 2.5 Pro |
| MedCode (Vals AI) | 50.6% | Not measured | Only Gemini 2.5 Pro measured |
| MMLU Pro (Artificial Analysis run) | 86.2% | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: science | 1480 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: medicine and healthcare | 1476 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: legal and government | 1480 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: business and finance | 1439 | Not measured | Only Gemini 2.5 Pro measured |
| FACTS Benchmark Suite | 61.5% | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Gemini 2.5 Pro | Qwen3.6 27B | Leader |
|---|---|---|---|
| Design Arena: websites | 1167 | Not measured | Only Gemini 2.5 Pro measured |
| Design Arena: UI components | 1137 | Not measured | Only Gemini 2.5 Pro measured |
| Design Arena: game dev | 1111 | Not measured | Only Gemini 2.5 Pro measured |
| Design Arena: data viz | 1222 | Not measured | Only Gemini 2.5 Pro measured |
| Design Arena: 3D | 1080 | Not measured | Only Gemini 2.5 Pro measured |
| Design Arena: all code categories | 1152 | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index23
| Benchmark | Gemini 2.5 Pro | Qwen3.6 27B | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 41.9% | 45.6% | Qwen3.6 27B |
| MedScribe (Vals AI) | 73.5% | Not measured | Only Gemini 2.5 Pro measured |
| IFBench | 48.7% | 67.6% | Qwen3.6 27B |
| Long context reasoning (AA-LCR) | 69.0% | 77.3% | Qwen3.6 27B |
| DTBench | 82.4% | 78.1% | Gemini 2.5 Pro |
| LMCA | 34.8% | 34.5% | Gemini 2.5 Pro |
| Creative writing (Lech Mazur) | 83.8% | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text | 1458 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: instruction following | 1437 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: creative writing | 1454 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision | 1263 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: English | 1458 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: non-English | 1452 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: Chinese | 1507 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: German | 1487 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: Japanese | 1461 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: Korean | 1434 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: French | 1473 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: Spanish | 1474 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: Russian | 1461 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: Polish | 1479 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: multi-turn | 1453 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: longer queries | 1449 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: writing and language | 1454 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: entertainment and media | 1441 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: English | 1261 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: Chinese | 1300 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: reading text | 1270 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: humor | 1262 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: captioning | 1304 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: recognizing things | 1283 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: creative writing | 1265 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Document | 1421 | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemini 2.5 Pro | 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
Qwen3.6 27B 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 | Google Gemini 2.5 Pro | Qwen3.6 27B |
|---|---|---|
| Maker | Qwen | |
| Weights | No public weights listed | Open weights |
| Released | June 17, 2025 | April 27, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $1.25 | $0.45 |
| Output $/Mtok | $10.00 | $2.70 |
| Coding index | 19.1 | 36.8 |
| Independent measurements behind it | 3 | 2 |
| Cost per solved task | $0.64 | $1.30 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemini 2.5 Pro better than Qwen3.6 27B for coding?
Pick Qwen3.6 27B for coding: it scores 36.8 on the Vibe Coding Index against 19.1 for Gemini 2.5 Pro, though on 2 benchmarks against 3. Pick Gemini 2.5 Pro for a lower cost per solved task ($0.64 against $1.30) and a larger context window (1.05M against 262K tokens).
Which is cheaper, Gemini 2.5 Pro or Qwen3.6 27B?
Gemini 2.5 Pro costs $1.25 per million input tokens and $10.00 per million output tokens; Qwen3.6 27B costs $0.45 and $2.70. Per solved SWE-bench Verified task, Gemini 2.5 Pro costs $0.64 and Qwen3.6 27B $1.30.
Which has the bigger context window, Gemini 2.5 Pro or Qwen3.6 27B?
Gemini 2.5 Pro, with 1.05M tokens against 262K.
How many benchmarks is the Gemini 2.5 Pro vs Qwen3.6 27B comparison based on?
22 benchmarks have a result for both models as of October 22, 2026. Gemini 2.5 Pro's coding index rests on 3 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.