GPT-4o-mini (2024-07-18) vs Qwen3.5 397B A17B
GPT-4o-mini (2024-07-18) costs 3.9x less per output token than Qwen3.5 397B A17B ($0.60 vs $2.34 per million). Updated October 23, 2026; 35 benchmarks have a result for both.
GPT-4o-mini (2024-07-18) or Qwen3.5 397B A17B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-4o-mini (2024-07-18) for output tokens 3.9x cheaper ($0.60 vs $2.34 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 35
- GPT-4o-mini (2024-07-18) only
- 11
- Qwen3.5 397B A17B only
- 31
- Like-for-like categories
- 5 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 397B A17B has a AA Intelligence Index result, 21.4.
Directional only
Web design
Design Arena: websites
No call
Only Qwen3.5 397B A17B has a Design Arena: websites result, 1190.
Directional only
Long documents
Listed context window
Qwen3.5 397B A17B
Qwen3.5 397B A17B takes 262K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-4o-mini (2024-07-18)
GPT-4o-mini (2024-07-18) $0.0004, Qwen3.5 397B A17B $0.0016 per turn.
List prices
At a glance
- Coding index
- GPT-4o-mini (2024-07-18) not benchmarked, Qwen3.5 397B A17B not benchmarked.
- Widest category
- Math, 43.5 points apart on average over 4 shared benchmarks.
- Widest single gap
- OTIS Mock AIME: GPT-4o-mini (2024-07-18) 6.9% against Qwen3.5 397B A17B 88.9%, 81.9 points apart on a 0 to 100 scale.
- Output price
- GPT-4o-mini (2024-07-18) $0.60 against Qwen3.5 397B A17B $2.34 per million output tokens, 3.9x apart.
- Context window
- GPT-4o-mini (2024-07-18) takes 128K tokens, Qwen3.5 397B A17B 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-mini (2024-07-18) | Bars | Qwen3.5 397B A17B | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 4 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 20 | ||
| All shared boards | 0 | 35 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | GPT-4o-mini (2024-07-18) | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| Aider polyglot | 3.6% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena WebDev | Not measured | 1400 | Only Qwen3.5 397B A17B measured |
| SciCode | Not measured | 44.8% | Only Qwen3.5 397B A17B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 40.9% | Only Qwen3.5 397B A17B measured |
| WeirdML | 11.8% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| ALE-Bench | Not measured | 621.9 | Only Qwen3.5 397B A17B measured |
| LMArena Text: coding | 1289 | 1465 | Qwen3.5 397B A17B |
| LMArena Text: software and IT | 1291 | 1463 | Qwen3.5 397B A17B |
| LMArena WebDev: React | Not measured | 1388 | Only Qwen3.5 397B A17B measured |
| LMArena WebDev: HTML | Not measured | 1409 | Only Qwen3.5 397B A17B measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 21.4 | Only Qwen3.5 397B A17B measured |
| Epoch Capabilities Index | Not measured | 146.8 | Only Qwen3.5 397B A17B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 95.6% | Only Qwen3.5 397B A17B measured |
| APEX-Agents | Not measured | 24.9% | Only Qwen3.5 397B A17B measured |
| BALROG | 17.4% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | GPT-4o-mini (2024-07-18) | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 89.3% | Only Qwen3.5 397B A17B measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 29.0% | Only Qwen3.5 397B A17B measured |
| GPQA Diamond (Epoch AI run) | 37.7% | 86.4% | Qwen3.5 397B A17B |
| SimpleBench | 10.7% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Chess puzzles | 3.0% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Mystery game puzzles | 12.0% | 18.0% | Qwen3.5 397B A17B |
| VPCT | 34.0% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: hard prompts | 1267 | 1448 | Qwen3.5 397B A17B |
| LMArena Text: expert | 1235 | 1462 | Qwen3.5 397B A17B |
| LMArena Text: hard prompts in English | 1278 | 1450 | Qwen3.5 397B A17B |
| LMArena Vision: diagrams | Not measured | 1277 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: homework | Not measured | 1292 | Only Qwen3.5 397B A17B measured |
| Neither model has a result on the other 11 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GPT-4o-mini (2024-07-18) | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| OTIS Mock AIME | 6.9% | 88.9% | Qwen3.5 397B A17B |
| FrontierMath, tiers 1 to 3 | 0.7% | 31.2% | Qwen3.5 397B A17B |
| MATH level 5 | 52.6% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: math | 1267 | 1454 | Qwen3.5 397B A17B |
| LMArena Text: mathematical work | 1271 | 1454 | Qwen3.5 397B A17B |
| MathArena | Not measured | 19.2% | Only Qwen3.5 397B A17B measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GPT-4o-mini (2024-07-18) | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| SimpleQA Verified | 8.3% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| MMLU | 76.7% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| GeoBench | 64.0% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: science | 1286 | 1457 | Qwen3.5 397B A17B |
| LMArena Text: medicine and healthcare | 1282 | 1455 | Qwen3.5 397B A17B |
| LMArena Text: legal and government | 1305 | 1441 | Qwen3.5 397B A17B |
| LMArena Text: business and finance | 1265 | 1435 | Qwen3.5 397B A17B |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1190 | Only Qwen3.5 397B A17B measured |
| Design Arena: UI components | Not measured | 1161 | Only Qwen3.5 397B A17B measured |
| Design Arena: game dev | Not measured | 1140 | Only Qwen3.5 397B A17B measured |
| Design Arena: data viz | Not measured | 1175 | Only Qwen3.5 397B A17B measured |
| Design Arena: 3D | Not measured | 1162 | Only Qwen3.5 397B A17B measured |
| Design Arena: all code categories | Not measured | 1179 | Only Qwen3.5 397B A17B measured |
| Design Arena: SVG | Not measured | 1139 | Only Qwen3.5 397B A17B measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index020
| Benchmark | GPT-4o-mini (2024-07-18) | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| IFBench | Not measured | 78.8% | Only Qwen3.5 397B A17B measured |
| Long context reasoning (AA-LCR) | Not measured | 77.3% | Only Qwen3.5 397B A17B measured |
| DTBench | Not measured | 87.5% | Only Qwen3.5 397B A17B measured |
| LMCA | Not measured | 37.9% | Only Qwen3.5 397B A17B measured |
| Creative writing (Lech Mazur) | 67.2% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text | 1286 | 1438 | Qwen3.5 397B A17B |
| LMArena Text: instruction following | 1258 | 1423 | Qwen3.5 397B A17B |
| LMArena Text: creative writing | 1268 | 1402 | Qwen3.5 397B A17B |
| LMArena Vision | 1066 | 1262 | Qwen3.5 397B A17B |
| LMArena Text: English | 1301 | 1443 | Qwen3.5 397B A17B |
| LMArena Text: non-English | 1266 | 1430 | Qwen3.5 397B A17B |
| LMArena Text: Chinese | 1265 | 1499 | Qwen3.5 397B A17B |
| LMArena Text: German | 1272 | 1444 | Qwen3.5 397B A17B |
| LMArena Text: Japanese | 1216 | 1430 | Qwen3.5 397B A17B |
| LMArena Text: Korean | 1195 | 1385 | Qwen3.5 397B A17B |
| LMArena Text: French | 1297 | 1462 | Qwen3.5 397B A17B |
| LMArena Text: Spanish | 1276 | 1441 | Qwen3.5 397B A17B |
| LMArena Text: Russian | 1275 | 1430 | Qwen3.5 397B A17B |
| LMArena Text: Polish | 1244 | 1448 | Qwen3.5 397B A17B |
| LMArena Text: multi-turn | 1285 | 1447 | Qwen3.5 397B A17B |
| LMArena Text: longer queries | 1289 | 1441 | Qwen3.5 397B A17B |
| LMArena Text: writing and language | 1282 | 1416 | Qwen3.5 397B A17B |
| LMArena Text: entertainment and media | 1253 | 1399 | Qwen3.5 397B A17B |
| LMArena Vision: English | 1072 | 1268 | Qwen3.5 397B A17B |
| LMArena Vision: Chinese | 1033 | 1314 | Qwen3.5 397B A17B |
| LMArena Vision: reading text | Not measured | 1271 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: humor | Not measured | 1262 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: captioning | Not measured | 1230 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: recognizing things | Not measured | 1265 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: creative writing | Not measured | 1260 | Only Qwen3.5 397B A17B measured |
| Neither model has a result on the other 18 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | Qwen3.5 397B A17B | 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
GPT-4o-mini (2024-07-18) costs less
Repository review
50K input + 3K output tokens
GPT-4o-mini (2024-07-18) costs less
Agent loop
200K input + 10K output tokens
GPT-4o-mini (2024-07-18) 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-mini (2024-07-18) | Qwen3.5 397B A17B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | July 18, 2024 | February 16, 2026 |
| Context window | 128K | 262K |
| Input $/Mtok | $0.15 | $0.39 |
| Output $/Mtok | $0.60 | $2.34 |
| 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-mini (2024-07-18) better than Qwen3.5 397B A17B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4o-mini (2024-07-18) for output tokens 3.9x cheaper ($0.60 vs $2.34 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, GPT-4o-mini (2024-07-18) or Qwen3.5 397B A17B?
GPT-4o-mini (2024-07-18) costs $0.15 per million input tokens and $0.60 per million output tokens; Qwen3.5 397B A17B costs $0.39 and $2.34. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4o-mini (2024-07-18) or Qwen3.5 397B A17B?
Qwen3.5 397B A17B, with 262K tokens against 128K.
How many benchmarks is the GPT-4o-mini (2024-07-18) vs Qwen3.5 397B A17B comparison based on?
35 benchmarks have a result for both models as of October 23, 2026. GPT-4o-mini (2024-07-18)'s coding index rests on 0 independent benchmarks and Qwen3.5 397B A17B's on 1. Where only one model has a result, the page says so instead of naming a winner.