GPT-4o (2024-08-06) vs Qwen3.6 27B
Qwen3.6 27B leads GPT-4o (2024-08-06) on the Artificial Analysis Intelligence Index, 21.4 to 7.7, and Qwen3.6 27B costs 3.7x less per output token ($2.70 vs $10.00 per million). Updated October 22, 2026; 13 benchmarks have a result for both.
GPT-4o (2024-08-06) or Qwen3.6 27B: which to pick
Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; GPT-4o (2024-08-06) has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 13
- GPT-4o (2024-08-06) only
- 45
- Qwen3.6 27B only
- 12
- 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
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 7.7.
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
Math
AIME
No call
Only GPT-4o (2024-08-06) has a AIME result, 14.0%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-4o (2024-08-06) has a MMLU Pro result, 74.1%.
Directional only
Long documents
Listed context window
Qwen3.6 27B
Qwen3.6 27B takes 262K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.6 27B
GPT-4o (2024-08-06) $0.0075, 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
- GPT-4o (2024-08-06) not benchmarked, Qwen3.6 27B 36.8 from 2 benchmarks.
- Widest category
- Agentic work, 65.2 points apart on average over 1 shared benchmark.
- Widest single gap
- OTIS Mock AIME: GPT-4o (2024-08-06) 6.4% against Qwen3.6 27B 91.1%, 84.7 points apart on a 0 to 100 scale.
- Output price
- GPT-4o (2024-08-06) $10.00 against Qwen3.6 27B $2.70 per million output tokens, 3.7x apart.
- Context window
- GPT-4o (2024-08-06) takes 128K 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 | GPT-4o (2024-08-06) | Bars | Qwen3.6 27B | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 3 | ||
| General | 0 | 2 | ||
| All shared boards | 0 | 13 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | GPT-4o (2024-08-06) | Qwen3.6 27B | Leader |
|---|---|---|---|
| Aider polyglot | 23.1% | Not measured | Only GPT-4o (2024-08-06) measured |
| SWE-bench Lite | 21.7% | Not measured | Only GPT-4o (2024-08-06) 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) | 31.7% | Not measured | Only GPT-4o (2024-08-06) measured |
| SciCode | Not measured | 42.8% | Only Qwen3.6 27B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 8.3% | 34.8% | Qwen3.6 27B |
| LMArena Text: coding | 1283 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Text: software and IT | 1278 | Not measured | Only GPT-4o (2024-08-06) measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4o (2024-08-06) | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.7 | 21.4 | Qwen3.6 27B |
| Epoch Capabilities Index | Not measured | 146.2 | Only Qwen3.6 27B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4o (2024-08-06) | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 18.5 | Only Qwen3.6 27B measured |
| τ²-Bench Telecom | 28.9% | 94.2% | Qwen3.6 27B |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-4o (2024-08-06) | Qwen3.6 27B | Leader |
|---|---|---|---|
| MMMU Pro (Vals AI) | 64.0% | Not measured | Only GPT-4o (2024-08-06) measured |
| GPQA Diamond (Artificial Analysis run) | 52.1% | 84.2% | Qwen3.6 27B |
| Humanity's Last Exam (Artificial Analysis run) | 2.3% | 23.1% | Qwen3.6 27B |
| GPQA Diamond (Epoch AI run) | 49.2% | 85.9% | Qwen3.6 27B |
| SimpleBench | 17.8% | Not measured | Only GPT-4o (2024-08-06) measured |
| Chess puzzles | 6.0% | Not measured | Only GPT-4o (2024-08-06) measured |
| Mystery game puzzles | Not measured | 7.0% | Only Qwen3.6 27B measured |
| LMArena Text: hard prompts | 1264 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Text: expert | 1241 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Text: hard prompts in English | 1277 | Not measured | Only GPT-4o (2024-08-06) measured |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4o (2024-08-06) | Qwen3.6 27B | Leader |
|---|---|---|---|
| AIME | 14.0% | Not measured | Only GPT-4o (2024-08-06) measured |
| MATH 500 (Vals AI) | 75.2% | Not measured | Only GPT-4o (2024-08-06) measured |
| MGSM (Vals AI) | 90.7% | Not measured | Only GPT-4o (2024-08-06) measured |
| OTIS Mock AIME | 6.4% | 91.1% | Qwen3.6 27B |
| FrontierMath, tiers 1 to 3 | 0.4% | 35.1% | Qwen3.6 27B |
| MATH level 5 | 53.3% | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Text: math | 1285 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Text: mathematical work | 1283 | Not measured | Only GPT-4o (2024-08-06) measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-4o (2024-08-06) | Qwen3.6 27B | Leader |
|---|---|---|---|
| MMLU Pro | 74.1% | Not measured | Only GPT-4o (2024-08-06) measured |
| LegalBench (Vals AI) | 80.1% | Not measured | Only GPT-4o (2024-08-06) measured |
| CaseLaw (Vals AI) | Not measured | 53.2% | Only Qwen3.6 27B measured |
| CorpFin (Vals AI) | 39.4% | 62.3% | Qwen3.6 27B |
| TaxEval (Vals AI) | 71.1% | 71.3% | Qwen3.6 27B |
| MortgageTax (Vals AI) | 61.0% | 68.3% | Qwen3.6 27B |
| MedQA (Vals AI) | 88.2% | Not measured | Only GPT-4o (2024-08-06) measured |
| SimpleQA Verified | 26.0% | Not measured | Only GPT-4o (2024-08-06) measured |
| MMLU | 84.3% | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Text: science | 1274 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Text: medicine and healthcare | 1252 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Text: legal and government | 1275 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Text: business and finance | 1232 | Not measured | Only GPT-4o (2024-08-06) 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 | GPT-4o (2024-08-06) | Qwen3.6 27B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4o (2024-08-06) | Qwen3.6 27B | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 45.6% | Only Qwen3.6 27B measured |
| IFBench | 36.0% | 67.6% | Qwen3.6 27B |
| Long context reasoning (AA-LCR) | 41.0% | 77.3% | Qwen3.6 27B |
| DTBench | Not measured | 78.1% | Only Qwen3.6 27B measured |
| LMCA | Not measured | 34.5% | Only Qwen3.6 27B measured |
| LMArena Text | 1283 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Text: instruction following | 1267 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Text: creative writing | 1274 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Vision | 1065 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Text: English | 1297 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Text: non-English | 1262 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Text: Chinese | 1254 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Text: German | 1257 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Text: Japanese | 1234 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Text: Korean | 1218 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Text: French | 1263 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Text: Spanish | 1269 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Text: Russian | 1272 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Text: multi-turn | 1279 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Text: longer queries | 1283 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Text: writing and language | 1290 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Text: entertainment and media | 1267 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Vision: English | 1072 | Not measured | Only GPT-4o (2024-08-06) measured |
| LMArena Vision: Chinese | 1009 | Not measured | Only GPT-4o (2024-08-06) measured |
| EQ-Bench 4 | Not measured | 1026 | Only Qwen3.6 27B measured |
| Neither model has a result on the other 23 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o (2024-08-06) | 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 | OpenAI GPT-4o (2024-08-06) | Qwen3.6 27B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | August 6, 2024 | April 27, 2026 |
| Context window | 128K | 262K |
| Input $/Mtok | $2.50 | $0.45 |
| Output $/Mtok | $10.00 | $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 GPT-4o (2024-08-06) better than Qwen3.6 27B for coding?
Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; GPT-4o (2024-08-06) has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, GPT-4o (2024-08-06) or Qwen3.6 27B?
GPT-4o (2024-08-06) costs $2.50 per million input tokens and $10.00 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, GPT-4o (2024-08-06) or Qwen3.6 27B?
Qwen3.6 27B, with 262K tokens against 128K.
How many benchmarks is the GPT-4o (2024-08-06) vs Qwen3.6 27B comparison based on?
13 benchmarks have a result for both models as of October 22, 2026. GPT-4o (2024-08-06)'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.