GPT-4o (2024-05-13) vs Qwen3.5-35B-A3B
Qwen3.5-35B-A3B leads GPT-4o (2024-05-13) on the Artificial Analysis Intelligence Index, 19.3 to 7.3, and Qwen3.5-35B-A3B costs 11.5x less per output token ($1.30 vs $15.00 per million). Updated October 23, 2026; 33 benchmarks have a result for both.
GPT-4o (2024-05-13) or Qwen3.5-35B-A3B: 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
- 33
- GPT-4o (2024-05-13) only
- 11
- Qwen3.5-35B-A3B only
- 13
- 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
Qwen3.5-35B-A3B
Qwen3.5-35B-A3B leads, 19.3 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
Qwen3.5-35B-A3B
Qwen3.5-35B-A3B takes 262K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-35B-A3B
GPT-4o (2024-05-13) $0.013, Qwen3.5-35B-A3B $0.0008 per turn.
List prices
At a glance
- Coding index
- GPT-4o (2024-05-13) not benchmarked, Qwen3.5-35B-A3B not benchmarked.
- Widest category
- Math, 34.9 points apart on average over 3 shared benchmarks.
- Widest single gap
- OTIS Mock AIME: GPT-4o (2024-05-13) 6.3% against Qwen3.5-35B-A3B 70.0%, 63.8 points apart on a 0 to 100 scale.
- Output price
- GPT-4o (2024-05-13) $15.00 against Qwen3.5-35B-A3B $1.30 per million output tokens, 11.5x apart.
- Context window
- GPT-4o (2024-05-13) takes 128K tokens, Qwen3.5-35B-A3B 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 | Qwen3.5-35B-A3B | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 6 | ||
| Math | 0 | 3 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 17 | ||
| All shared boards | 0 | 33 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | GPT-4o (2024-05-13) | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1254 | Only Qwen3.5-35B-A3B 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 | 26.5% | Only Qwen3.5-35B-A3B measured |
| ALE-Bench | Not measured | 221.8 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: coding | 1297 | 1410 | Qwen3.5-35B-A3B |
| LMArena Text: software and IT | 1297 | 1415 | Qwen3.5-35B-A3B |
| LMArena WebDev: React | Not measured | 1238 | Only Qwen3.5-35B-A3B measured |
| LMArena WebDev: HTML | Not measured | 1301 | Only Qwen3.5-35B-A3B measured |
| Neither model has a result on the other 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4o (2024-05-13) | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.3 | 19.3 | Qwen3.5-35B-A3B |
| Epoch Capabilities Index | Not measured | 142.5 | Only Qwen3.5-35B-A3B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 89.2% | Only Qwen3.5-35B-A3B 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 | $463 | Only Qwen3.5-35B-A3B measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | GPT-4o (2024-05-13) | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| GPQA Diamond | 50.2% | Not measured | Only GPT-4o (2024-05-13) measured |
| GPQA Diamond (Artificial Analysis run) | 52.6% | 84.5% | Qwen3.5-35B-A3B |
| Humanity's Last Exam (Artificial Analysis run) | 1.8% | 21.0% | Qwen3.5-35B-A3B |
| GPQA Diamond (Epoch AI run) | 48.9% | 83.5% | Qwen3.5-35B-A3B |
| LMArena Text: hard prompts | 1281 | 1400 | Qwen3.5-35B-A3B |
| LMArena Text: expert | 1250 | 1407 | Qwen3.5-35B-A3B |
| LMArena Text: hard prompts in English | 1293 | 1411 | Qwen3.5-35B-A3B |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-4o (2024-05-13) | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| OTIS Mock AIME | 6.3% | 70.0% | Qwen3.5-35B-A3B |
| MATH level 5 | 51.0% | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: math | 1284 | 1404 | Qwen3.5-35B-A3B |
| LMArena Text: mathematical work | 1284 | 1410 | Qwen3.5-35B-A3B |
| MathArena | Not measured | 14.7% | Only Qwen3.5-35B-A3B 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) | Qwen3.5-35B-A3B | 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 | 1413 | Qwen3.5-35B-A3B |
| LMArena Text: medicine and healthcare | 1278 | 1421 | Qwen3.5-35B-A3B |
| LMArena Text: legal and government | 1314 | 1398 | Qwen3.5-35B-A3B |
| LMArena Text: business and finance | 1261 | 1394 | Qwen3.5-35B-A3B |
| 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) | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index017
| Benchmark | GPT-4o (2024-05-13) | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| IFBench | Not measured | 72.5% | Only Qwen3.5-35B-A3B measured |
| Long context reasoning (AA-LCR) | Not measured | 72.0% | Only Qwen3.5-35B-A3B measured |
| DTBench | Not measured | 80.0% | Only Qwen3.5-35B-A3B measured |
| LMCA | Not measured | 29.5% | Only Qwen3.5-35B-A3B measured |
| LMArena Text | 1300 | 1395 | Qwen3.5-35B-A3B |
| LMArena Text: instruction following | 1278 | 1379 | Qwen3.5-35B-A3B |
| LMArena Text: creative writing | 1291 | 1347 | Qwen3.5-35B-A3B |
| LMArena Vision | 1137 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Text: English | 1312 | 1407 | Qwen3.5-35B-A3B |
| LMArena Text: non-English | 1283 | 1378 | Qwen3.5-35B-A3B |
| LMArena Text: Chinese | 1277 | 1456 | Qwen3.5-35B-A3B |
| LMArena Text: German | 1282 | 1367 | Qwen3.5-35B-A3B |
| LMArena Text: Japanese | 1257 | 1326 | Qwen3.5-35B-A3B |
| LMArena Text: Korean | 1234 | 1354 | Qwen3.5-35B-A3B |
| LMArena Text: French | 1304 | 1413 | Qwen3.5-35B-A3B |
| LMArena Text: Spanish | 1292 | 1390 | Qwen3.5-35B-A3B |
| LMArena Text: Russian | 1285 | 1377 | Qwen3.5-35B-A3B |
| LMArena Text: Polish | 1365 | 1385 | Qwen3.5-35B-A3B |
| LMArena Text: multi-turn | 1302 | 1390 | Qwen3.5-35B-A3B |
| LMArena Text: longer queries | 1288 | 1389 | Qwen3.5-35B-A3B |
| LMArena Text: writing and language | 1298 | 1365 | Qwen3.5-35B-A3B |
| LMArena Text: entertainment and media | 1289 | 1347 | Qwen3.5-35B-A3B |
| LMArena Vision: English | 1147 | Not measured | Only GPT-4o (2024-05-13) measured |
| LMArena Vision: Chinese | 1132 | Not measured | Only GPT-4o (2024-05-13) measured |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o (2024-05-13) | Qwen3.5-35B-A3B | 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
Qwen3.5-35B-A3B costs less
Repository review
50K input + 3K output tokens
Qwen3.5-35B-A3B costs less
Agent loop
200K input + 10K output tokens
Qwen3.5-35B-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 | OpenAI GPT-4o (2024-05-13) | Qwen3.5-35B-A3B |
|---|---|---|
| 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 | $0.1625 |
| Output $/Mtok | $15.00 | $1.30 |
| 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 Qwen3.5-35B-A3B 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 Qwen3.5-35B-A3B?
GPT-4o (2024-05-13) costs $5.00 per million input tokens and $15.00 per million output tokens; Qwen3.5-35B-A3B costs $0.1625 and $1.30. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4o (2024-05-13) or Qwen3.5-35B-A3B?
Qwen3.5-35B-A3B, with 262K tokens against 128K.
How many benchmarks is the GPT-4o (2024-05-13) vs Qwen3.5-35B-A3B comparison based on?
33 benchmarks have a result for both models as of October 23, 2026. GPT-4o (2024-05-13)'s coding index rests on 0 independent benchmarks and Qwen3.5-35B-A3B's on 1. Where only one model has a result, the page says so instead of naming a winner.