GPT-4o vs Qwen3.5-35B-A3B
Qwen3.5-35B-A3B costs 7.7x less per output token than GPT-4o ($1.30 vs $10.00 per million). Updated October 22, 2026; 8 benchmarks have a result for both.
GPT-4o 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 for work measured by Terminal-Bench Hard (Artificial Analysis run) or τ²-Bench Telecom, where it leads.
- Shared results
- 8
- GPT-4o only
- 19
- Qwen3.5-35B-A3B only
- 37
- Like-for-like categories
- 4 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
No call
Only Qwen3.5-35B-A3B has a AA Intelligence Index result, 19.3.
Directional only
Web design
Design Arena: websites
No call
Only GPT-4o has a Design Arena: websites result, 831.
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 $0.0075, Qwen3.5-35B-A3B $0.0008 per turn.
List prices
At a glance
- Coding index
- GPT-4o not benchmarked, Qwen3.5-35B-A3B not benchmarked.
- Widest category
- Agentic work, 64.0 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GPT-4o 25.1% against Qwen3.5-35B-A3B 89.2%, 64.0 points apart on a 0 to 100 scale.
- Output price
- GPT-4o $10.00 against Qwen3.5-35B-A3B $1.30 per million output tokens, 7.7x apart.
- Context window
- GPT-4o 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 | Bars | Qwen3.5-35B-A3B | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| General | 0 | 4 | ||
| All shared boards | 0 | 8 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | GPT-4o | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| SWE-bench Verified | 38.8% | Not measured | Only GPT-4o measured |
| SWE-bench Lite | 38.0% | Not measured | Only GPT-4o measured |
| SWE-bench (full test set) | 18.8% | Not measured | Only GPT-4o measured |
| SWE-bench Multimodal | 30.4% | Not measured | Only GPT-4o measured |
| LMArena WebDev | Not measured | 1254 | Only Qwen3.5-35B-A3B measured |
| LiveCodeBench (Artificial Analysis run) | 30.9% | Not measured | Only GPT-4o measured |
| Terminal-Bench Hard (Artificial Analysis run) | 8.3% | 26.5% | Qwen3.5-35B-A3B |
| ALE-Bench | Not measured | 221.8 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: coding | Not measured | 1410 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: software and IT | Not measured | 1415 | Only Qwen3.5-35B-A3B measured |
| LMArena WebDev: React | Not measured | 1238 | Only Qwen3.5-35B-A3B measured |
| LMArena WebDev: HTML | Not measured | 1301 | Only Qwen3.5-35B-A3B measured |
| SciCode | 1.5% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4o | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 19.3 | Only Qwen3.5-35B-A3B measured |
| Epoch Capabilities Index | Not measured | 142.5 | Only Qwen3.5-35B-A3B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4o | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 25.1% | 89.2% | Qwen3.5-35B-A3B |
| GDPval | 9.9% | Not measured | Only GPT-4o 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 index02
| Benchmark | GPT-4o | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 54.3% | 84.5% | Qwen3.5-35B-A3B |
| Humanity's Last Exam (Artificial Analysis run) | 2.4% | 21.0% | Qwen3.5-35B-A3B |
| GPQA Diamond (Epoch AI run) | Not measured | 83.5% | Only Qwen3.5-35B-A3B measured |
| ARC-AGI | 4.5% | Not measured | Only GPT-4o measured |
| ARC-AGI-2 | 0.0% | Not measured | Only GPT-4o measured |
| LMArena Text: hard prompts | Not measured | 1400 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: expert | Not measured | 1408 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: hard prompts in English | Not measured | 1411 | Only Qwen3.5-35B-A3B measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-4o | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| AA Math Index | 6.0 | Not measured | Only GPT-4o measured |
| AIME 2025 (Artificial Analysis run) | 6.0% | Not measured | Only GPT-4o measured |
| OTIS Mock AIME | Not measured | 70.0% | Only Qwen3.5-35B-A3B measured |
| LMArena Text: math | Not measured | 1404 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: mathematical work | Not measured | 1410 | Only Qwen3.5-35B-A3B measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-4o | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 74.8% | Not measured | Only GPT-4o measured |
| MMLU | 88.1% | Not measured | Only GPT-4o measured |
| LMArena Text: science | Not measured | 1413 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: medicine and healthcare | Not measured | 1421 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: legal and government | Not measured | 1399 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: business and finance | Not measured | 1394 | Only Qwen3.5-35B-A3B measured |
| Neither model has a result on the other 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-4o | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| Design Arena: websites | 831 | Not measured | Only GPT-4o measured |
| Design Arena: UI components | 882 | Not measured | Only GPT-4o measured |
| Design Arena: game dev | 906 | Not measured | Only GPT-4o measured |
| Design Arena: data viz | 858 | Not measured | Only GPT-4o measured |
| Design Arena: 3D | 868 | Not measured | Only GPT-4o measured |
| Design Arena: all code categories | 860 | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GPT-4o | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| IFBench | 34.3% | 72.5% | Qwen3.5-35B-A3B |
| Long context reasoning (AA-LCR) | 49.3% | 72.0% | Qwen3.5-35B-A3B |
| DTBench | 64.5% | 80.0% | Qwen3.5-35B-A3B |
| LMCA | 16.6% | 29.5% | Qwen3.5-35B-A3B |
| LMArena Text | Not measured | 1395 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: instruction following | Not measured | 1379 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: creative writing | Not measured | 1347 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: English | Not measured | 1407 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: non-English | Not measured | 1378 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: Chinese | Not measured | 1457 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: German | Not measured | 1367 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: Japanese | Not measured | 1326 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: Korean | Not measured | 1355 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: French | Not measured | 1412 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: Spanish | Not measured | 1390 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: Russian | Not measured | 1376 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: Polish | Not measured | 1385 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: multi-turn | Not measured | 1390 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: longer queries | Not measured | 1389 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: writing and language | Not measured | 1365 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: entertainment and media | Not measured | 1347 | Only Qwen3.5-35B-A3B measured |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o | 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 | 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 | $2.50 | $0.1625 |
| Output $/Mtok | $10.00 | $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 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 for work measured by Terminal-Bench Hard (Artificial Analysis run) or τ²-Bench Telecom, where it leads.
Which is cheaper, GPT-4o or Qwen3.5-35B-A3B?
GPT-4o costs $2.50 per million input tokens and $10.00 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 or Qwen3.5-35B-A3B?
Qwen3.5-35B-A3B, with 262K tokens against 128K.
How many benchmarks is the GPT-4o vs Qwen3.5-35B-A3B comparison based on?
8 benchmarks have a result for both models as of October 22, 2026. GPT-4o'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.