GPT-4o-mini (2024-07-18) vs Qwen3.5-122B-A10B
Updated October 21, 2026. 32 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Qwen3.5-122B-A10B for coding: it scores 22.9 on the Vibe Coding Index against 3.6 for GPT-4o-mini (2024-07-18).
Pick GPT-4o-mini (2024-07-18) when output tokens cost 3.5x less.
- Shared results
- 32
- GPT-4o-mini (2024-07-18) only
- 14
- Qwen3.5-122B-A10B only
- 19
- Like-for-like categories
- 5 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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, four public coding benchmarks
Qwen3.5-122B-A10B
Qwen3.5-122B-A10B scores 22.9 against 3.6.
One benchmark behind each score
Intelligence
AA Intelligence Index
No call
Only Qwen3.5-122B-A10B has a AA Intelligence Index result, 17.7.
Directional only
Long documents
Listed context window
Qwen3.5-122B-A10B
Qwen3.5-122B-A10B 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, Qwen3.5-122B-A10B $0.0013 per turn.
List prices
At a glance
- Coding index
- GPT-4o-mini (2024-07-18) 3.6 from 1 benchmark, Qwen3.5-122B-A10B 22.9 from 1 benchmark.
- Widest category
- Math, 27.0 points apart on average over 2 shared benchmarks.
- Widest single gap
- LMArena Vision: Chinese: GPT-4o-mini (2024-07-18) 1033 against Qwen3.5-122B-A10B 1301, 44.6 points apart on a 0 to 100 scale.
- Output price
- GPT-4o-mini (2024-07-18) $0.60 against Qwen3.5-122B-A10B $2.08 per million output tokens, 3.5x apart.
- Context window
- GPT-4o-mini (2024-07-18) takes 128K tokens, Qwen3.5-122B-A10B 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 | Qwen3.5-122B-A10B | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Reasoning | 0 | 4 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 20 | ||
| All shared boards | 0 | 32 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index02
| Benchmark | GPT-4o-mini (2024-07-18) | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| Aider polyglot | 3.6% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena WebDev | Not measured | 1361 | Only Qwen3.5-122B-A10B measured |
| AA Coding Index | Not measured | 43.3 | Only Qwen3.5-122B-A10B measured |
| SciCode | Not measured | 39.7% | Only Qwen3.5-122B-A10B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 31.1% | Only Qwen3.5-122B-A10B measured |
| WeirdML | 11.8% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: coding | 1290 | 1436 | Qwen3.5-122B-A10B |
| LMArena Text: software and IT | 1291 | 1436 | Qwen3.5-122B-A10B |
| LMArena WebDev: React | Not measured | 1353 | Only Qwen3.5-122B-A10B measured |
| LMArena WebDev: HTML | Not measured | 1351 | Only Qwen3.5-122B-A10B measured |
| Neither model has a result on the other 28 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 17.7 | Only Qwen3.5-122B-A10B measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 93.6% | Only Qwen3.5-122B-A10B measured |
| BALROG | 17.4% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GPT-4o-mini (2024-07-18) | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 85.7% | Only Qwen3.5-122B-A10B measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 25.2% | Only Qwen3.5-122B-A10B measured |
| GPQA Diamond (Epoch AI run) | 37.7% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| 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% | 17.0% | Qwen3.5-122B-A10B |
| VPCT | 34.0% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: hard prompts | 1267 | 1421 | Qwen3.5-122B-A10B |
| LMArena Text: expert | 1234 | 1431 | Qwen3.5-122B-A10B |
| LMArena Text: hard prompts in English | 1278 | 1431 | Qwen3.5-122B-A10B |
| LMArena Vision: diagrams | Not measured | 1249 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: homework | Not measured | 1261 | Only Qwen3.5-122B-A10B measured |
| Neither model has a result on the other 9 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4o-mini (2024-07-18) | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| OTIS Mock AIME | 6.9% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| FrontierMath, tiers 1 to 3 | 0.7% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| MATH level 5 | 52.6% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: math | 1267 | 1431 | Qwen3.5-122B-A10B |
| LMArena Text: mathematical work | 1271 | 1431 | Qwen3.5-122B-A10B |
| 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) | Qwen3.5-122B-A10B | 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 | 1432 | Qwen3.5-122B-A10B |
| LMArena Text: medicine and healthcare | 1282 | 1423 | Qwen3.5-122B-A10B |
| LMArena Text: legal and government | 1305 | 1422 | Qwen3.5-122B-A10B |
| LMArena Text: business and finance | 1265 | 1414 | Qwen3.5-122B-A10B |
| Neither model has a result on the other 10 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | Qwen3.5-122B-A10B | 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-mini (2024-07-18) | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| IFBench | Not measured | 75.7% | Only Qwen3.5-122B-A10B measured |
| Long context reasoning (AA-LCR) | Not measured | 76.3% | Only Qwen3.5-122B-A10B measured |
| DTBench | Not measured | 84.3% | Only Qwen3.5-122B-A10B measured |
| LMCA | Not measured | 32.2% | Only Qwen3.5-122B-A10B measured |
| Creative writing (Lech Mazur) | 67.2% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text | 1286 | 1417 | Qwen3.5-122B-A10B |
| LMArena Text: instruction following | 1258 | 1399 | Qwen3.5-122B-A10B |
| LMArena Text: creative writing | 1268 | 1368 | Qwen3.5-122B-A10B |
| LMArena Vision | 1066 | 1244 | Qwen3.5-122B-A10B |
| LMArena Text: English | 1301 | 1428 | Qwen3.5-122B-A10B |
| LMArena Text: non-English | 1266 | 1401 | Qwen3.5-122B-A10B |
| LMArena Text: Chinese | 1265 | 1463 | Qwen3.5-122B-A10B |
| LMArena Text: German | 1272 | 1425 | Qwen3.5-122B-A10B |
| LMArena Text: Japanese | 1216 | 1367 | Qwen3.5-122B-A10B |
| LMArena Text: Korean | 1195 | 1353 | Qwen3.5-122B-A10B |
| LMArena Text: French | 1297 | 1443 | Qwen3.5-122B-A10B |
| LMArena Text: Spanish | 1276 | 1425 | Qwen3.5-122B-A10B |
| LMArena Text: Russian | 1275 | 1400 | Qwen3.5-122B-A10B |
| LMArena Text: Polish | 1244 | 1398 | Qwen3.5-122B-A10B |
| LMArena Text: multi-turn | 1285 | 1416 | Qwen3.5-122B-A10B |
| LMArena Text: longer queries | 1289 | 1410 | Qwen3.5-122B-A10B |
| LMArena Text: writing and language | 1282 | 1387 | Qwen3.5-122B-A10B |
| LMArena Text: entertainment and media | 1253 | 1366 | Qwen3.5-122B-A10B |
| LMArena Vision: English | 1072 | 1249 | Qwen3.5-122B-A10B |
| LMArena Vision: Chinese | 1033 | 1301 | Qwen3.5-122B-A10B |
| LMArena Vision: reading text | Not measured | 1251 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: humor | Not measured | 1243 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: creative writing | Not measured | 1220 | Only Qwen3.5-122B-A10B 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) | Qwen3.5-122B-A10B | 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) | Qwen3.5-122B-A10B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | July 18, 2024 | February 25, 2026 |
| Context window | 128K | 262K |
| Input $/Mtok | $0.15 | $0.26 |
| Output $/Mtok | $0.60 | $2.08 |
| Coding index | 3.6 | 22.9 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4o-mini (2024-07-18) better than Qwen3.5-122B-A10B for coding?
Pick Qwen3.5-122B-A10B for coding: it scores 22.9 on the Vibe Coding Index against 3.6 for GPT-4o-mini (2024-07-18). Pick GPT-4o-mini (2024-07-18) when output tokens cost 3.5x less.
Which is cheaper, GPT-4o-mini (2024-07-18) or Qwen3.5-122B-A10B?
GPT-4o-mini (2024-07-18) costs $0.15 per million input tokens and $0.60 per million output tokens; Qwen3.5-122B-A10B costs $0.26 and $2.08. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4o-mini (2024-07-18) or Qwen3.5-122B-A10B?
Qwen3.5-122B-A10B, with 262K tokens against 128K.
How many benchmarks is the GPT-4o-mini (2024-07-18) vs Qwen3.5-122B-A10B comparison based on?
32 benchmarks have a result for both models as of October 21, 2026. GPT-4o-mini (2024-07-18)'s coding index rests on 1 independent benchmark and Qwen3.5-122B-A10B's on 1. Where only one model has a result, the page says so instead of naming a winner.