GPT-4o (2024-05-13) vs Qwen3.5-27B
Qwen3.5-27B leads GPT-4o (2024-05-13) on the Artificial Analysis Intelligence Index, 22.9 to 7.3, and Qwen3.5-27B costs 9.6x less per output token ($1.56 vs $15.00 per million). Updated October 22, 2026; 34 benchmarks have a result for both.
GPT-4o (2024-05-13) or Qwen3.5-27B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-4o (2024-05-13) for work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 34
- GPT-4o (2024-05-13) only
- 10
- Qwen3.5-27B only
- 20
- Like-for-like categories
- 6 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
Qwen3.5-27B
Qwen3.5-27B leads, 22.9 against 7.3.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GPT-4o (2024-05-13) has a GPQA Diamond result, 50.2%.
Directional only
Long documents
Listed context window
Qwen3.5-27B
Qwen3.5-27B takes 262K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-27B
GPT-4o (2024-05-13) $0.013, Qwen3.5-27B $0.0010 per turn.
List prices
At a glance
- Coding index
- GPT-4o (2024-05-13) not benchmarked, Qwen3.5-27B not benchmarked.
- Widest category
- Reasoning, 26.0 points apart on average over 5 shared benchmarks.
- Widest single gap
- LMArena Text: Chinese: GPT-4o (2024-05-13) 1277 against Qwen3.5-27B 1478, 33.5 points apart on a 0 to 100 scale.
- Output price
- GPT-4o (2024-05-13) $15.00 against Qwen3.5-27B $1.56 per million output tokens, 9.6x apart.
- Context window
- GPT-4o (2024-05-13) takes 128K tokens, Qwen3.5-27B 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 (2024-05-13) | Bars | Qwen3.5-27B | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 20 | ||
| All shared boards | 0 | 34 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | GPT-4o (2024-05-13) | Qwen3.5-27B | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1358 | Only Qwen3.5-27B measured |
| AA Coding Index | 24.2 | Not measured | Only GPT-4o (2024-05-13) measured |
| LiveCodeBench (Artificial Analysis run) | 33.4% | Not measured | Only GPT-4o (2024-05-13) measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 32.6% | Only Qwen3.5-27B measured |
| WeirdML | Not measured | 39.5% | Only Qwen3.5-27B measured |
| ALE-Bench | Not measured | 349.4 | Only Qwen3.5-27B measured |
| LMArena Text: coding | 1297 | 1427 | Qwen3.5-27B |
| LMArena Text: software and IT | 1297 | 1431 | Qwen3.5-27B |
| LMArena WebDev: React | Not measured | 1346 | Only Qwen3.5-27B measured |
| LMArena WebDev: HTML | Not measured | 1386 | Only Qwen3.5-27B 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 (2024-05-13) | Qwen3.5-27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.3 | 22.9 | Qwen3.5-27B |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | Qwen3.5-27B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 93.9% | Only Qwen3.5-27B measured |
| METR time horizons | 40.8% | Not measured | Only GPT-4o (2024-05-13) measured |
| BALROG | 32.3% | Not measured | Only GPT-4o (2024-05-13) measured |
| Vending-Bench 2 | Not measured | $202 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | GPT-4o (2024-05-13) | Qwen3.5-27B | Leader |
|---|---|---|---|
| GPQA Diamond | 50.2% | Not measured | Only GPT-4o (2024-05-13) measured |
| GPQA Diamond (Artificial Analysis run) | 52.6% | 85.8% | Qwen3.5-27B |
| Humanity's Last Exam (Artificial Analysis run) | 1.8% | 23.9% | Qwen3.5-27B |
| GPQA Diamond (Epoch AI run) | 48.9% | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: hard prompts | 1281 | 1415 | Qwen3.5-27B |
| LMArena Text: expert | 1250 | 1427 | Qwen3.5-27B |
| LMArena Text: hard prompts in English | 1293 | 1429 | Qwen3.5-27B |
| LMArena Vision: diagrams | Not measured | 1249 | Only Qwen3.5-27B measured |
| LMArena Vision: homework | Not measured | 1256 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4o (2024-05-13) | Qwen3.5-27B | Leader |
|---|---|---|---|
| OTIS Mock AIME | 6.3% | Not measured | Only GPT-4o (2024-05-13) measured |
| MATH level 5 | 51.0% | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: math | 1284 | 1429 | Qwen3.5-27B |
| LMArena Text: mathematical work | 1284 | 1429 | Qwen3.5-27B |
| MathArena | Not measured | 15.8% | Only Qwen3.5-27B measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GPT-4o (2024-05-13) | Qwen3.5-27B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 74.0% | Not measured | Only GPT-4o (2024-05-13) measured |
| MMLU | 84.2% | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: science | 1295 | 1437 | Qwen3.5-27B |
| LMArena Text: medicine and healthcare | 1278 | 1425 | Qwen3.5-27B |
| LMArena Text: legal and government | 1314 | 1405 | Qwen3.5-27B |
| LMArena Text: business and finance | 1262 | 1399 | Qwen3.5-27B |
| Neither model has a result on the other 14 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | Qwen3.5-27B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index020
| Benchmark | GPT-4o (2024-05-13) | Qwen3.5-27B | Leader |
|---|---|---|---|
| IFBench | Not measured | 75.6% | Only Qwen3.5-27B measured |
| Long context reasoning (AA-LCR) | Not measured | 77.7% | Only Qwen3.5-27B measured |
| DTBench | Not measured | 82.4% | Only Qwen3.5-27B measured |
| LMCA | Not measured | 34.0% | Only Qwen3.5-27B measured |
| LMArena Text | 1300 | 1409 | Qwen3.5-27B |
| LMArena Text: instruction following | 1278 | 1393 | Qwen3.5-27B |
| LMArena Text: creative writing | 1291 | 1362 | Qwen3.5-27B |
| LMArena Vision | 1137 | 1241 | Qwen3.5-27B |
| LMArena Text: English | 1312 | 1425 | Qwen3.5-27B |
| LMArena Text: non-English | 1283 | 1390 | Qwen3.5-27B |
| LMArena Text: Chinese | 1277 | 1478 | Qwen3.5-27B |
| LMArena Text: German | 1283 | 1392 | Qwen3.5-27B |
| LMArena Text: Japanese | 1257 | 1346 | Qwen3.5-27B |
| LMArena Text: Korean | 1234 | 1359 | Qwen3.5-27B |
| LMArena Text: French | 1304 | 1410 | Qwen3.5-27B |
| LMArena Text: Spanish | 1292 | 1407 | Qwen3.5-27B |
| LMArena Text: Russian | 1285 | 1390 | Qwen3.5-27B |
| LMArena Text: Polish | 1365 | 1375 | Qwen3.5-27B |
| LMArena Text: multi-turn | 1302 | 1410 | Qwen3.5-27B |
| LMArena Text: longer queries | 1288 | 1413 | Qwen3.5-27B |
| LMArena Text: writing and language | 1298 | 1380 | Qwen3.5-27B |
| LMArena Text: entertainment and media | 1289 | 1363 | Qwen3.5-27B |
| LMArena Vision: English | 1147 | 1246 | Qwen3.5-27B |
| LMArena Vision: Chinese | 1132 | 1301 | Qwen3.5-27B |
| LMArena Vision: reading text | Not measured | 1246 | Only Qwen3.5-27B measured |
| LMArena Vision: humor | Not measured | 1227 | Only Qwen3.5-27B measured |
| LMArena Vision: captioning | Not measured | 1170 | Only Qwen3.5-27B measured |
| LMArena Vision: recognizing things | Not measured | 1210 | Only Qwen3.5-27B measured |
| LMArena Vision: creative writing | Not measured | 1224 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 19 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | Qwen3.5-27B | 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
Qwen3.5-27B costs less
Repository review
50K input + 3K output tokens
Qwen3.5-27B costs less
Agent loop
200K input + 10K output tokens
Qwen3.5-27B 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 (2024-05-13) | Qwen3.5-27B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | May 13, 2024 | February 25, 2026 |
| Context window | 128K | 262K |
| Input $/Mtok | $5.00 | $0.195 |
| Output $/Mtok | $15.00 | $1.56 |
| 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 GPT-4o (2024-05-13) better than Qwen3.5-27B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4o (2024-05-13) for work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, GPT-4o (2024-05-13) or Qwen3.5-27B?
GPT-4o (2024-05-13) costs $5.00 per million input tokens and $15.00 per million output tokens; Qwen3.5-27B costs $0.195 and $1.56. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4o (2024-05-13) or Qwen3.5-27B?
Qwen3.5-27B, with 262K tokens against 128K.
How many benchmarks is the GPT-4o (2024-05-13) vs Qwen3.5-27B comparison based on?
34 benchmarks have a result for both models as of October 22, 2026. GPT-4o (2024-05-13)'s coding index rests on 0 independent benchmarks and Qwen3.5-27B's on 1. Where only one model has a result, the page says so instead of naming a winner.