Grok 4.7 vs Qwen3.5-35B-A3B
Updated October 21, 2026. 40 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Grok 4.7 for coding: it scores 75.0 on the Vibe Coding Index against 7.7 for Qwen3.5-35B-A3B.
Pick Qwen3.5-35B-A3B when output tokens cost 6.0x less, or when what matters is LMArena Text: medicine and healthcare or LMArena Text: business and finance, where it leads.
- Shared results
- 40
- Grok 4.7 only
- 59
- Qwen3.5-35B-A3B only
- 5
- Like-for-like categories
- 7 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
Grok 4.7
Grok 4.7 scores 75.0 against 7.7.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
Grok 4.7
Grok 4.7 leads, 46.4 against 19.3.
Same board, both measured
Web design
Design Arena: websites
No call
Only Grok 4.7 has a Design Arena: websites result, 1255.
Directional only
General
Vals Index
No call
Only Grok 4.7 has a Vals Index result, 58.6%.
Directional only
Long documents
Listed context window
Grok 4.7
Grok 4.7 takes 500K tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-35B-A3B
Grok 4.7 $0.0050, Qwen3.5-35B-A3B $0.0006 per turn.
List prices
At a glance
- Coding index
- Grok 4.7 75.0 from 2 benchmarks, Qwen3.5-35B-A3B 7.7 from 1 benchmark.
- Widest category
- Agentic work, 63.0 points apart on average over 1 shared benchmark.
- Widest single gap
- Vending-Bench 2: Grok 4.7 $10,537 against Qwen3.5-35B-A3B $463, 63.0 points apart on a 0 to 100 scale.
- Output price
- Grok 4.7 $6.00 against Qwen3.5-35B-A3B $1.00 per million output tokens, 6.0x apart.
- Context window
- Grok 4.7 takes 500K 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 | Grok 4.7 | Bars | Qwen3.5-35B-A3B | Ties |
|---|---|---|---|---|
| Coding | 5 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 5 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 2 | 2 | ||
| General | 18 | 2 | ||
| All shared boards | 36 | 4 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index50
| Benchmark | Grok 4.7 | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| LiveBench coding | 84.5% | Not measured | Only Grok 4.7 measured |
| LMArena WebDev | 1637 | 1254 | Grok 4.7 |
| Terminal-Bench 4 (Vals AI) | 28.8% | Not measured | Only Grok 4.7 measured |
| Vibe Code Bench (Vals AI) | 86.2% | Not measured | Only Grok 4.7 measured |
| Code migration (Vals AI) | 44.8% | Not measured | Only Grok 4.7 measured |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only Grok 4.7 measured |
| IOI (Vals AI) | 57.7% | Not measured | Only Grok 4.7 measured |
| Terminal-Bench 2.1 (Vals AI) | 73.4% | Not measured | Only Grok 4.7 measured |
| SWE-bench Multimodal (Vals AI) | 42.9% | Not measured | Only Grok 4.7 measured |
| CyberBench (Vals AI) | 69.5% | Not measured | Only Grok 4.7 measured |
| SciCode | 57.8% | Not measured | Only Grok 4.7 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 26.5% | Only Qwen3.5-35B-A3B measured |
| FrontierCode | 47.6% | Not measured | Only Grok 4.7 measured |
| FrontierSWE | 29.5% | Not measured | Only Grok 4.7 measured |
| ALE-Bench | Not measured | 221.8 | Only Qwen3.5-35B-A3B measured |
| LMArena Text: coding | 1432 | 1410 | Grok 4.7 |
| LMArena Text: software and IT | 1421 | 1415 | Grok 4.7 |
| LMArena WebDev: React | 1663 | 1238 | Grok 4.7 |
| LMArena WebDev: HTML | 1627 | 1301 | Grok 4.7 |
| LMArena WebDev: from an image | 1586 | Not measured | Only Grok 4.7 measured |
| Neither model has a result on the other 18 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Grok 4.7 | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| AA Intelligence Index | 46.4 | 19.3 | Grok 4.7 |
| Epoch Capabilities Index | 154.4 | 142.5 | Grok 4.7 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Grok 4.7 | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| LiveBench research | 86.4% | Not measured | Only Grok 4.7 measured |
| LiveBench browser | 60.0% | Not measured | Only Grok 4.7 measured |
| LiveBench long agentic | 50.1% | Not measured | Only Grok 4.7 measured |
| SREBench (Vals AI) | 3.8% | Not measured | Only Grok 4.7 measured |
| Finance Agent (Vals AI) | 52.3% | Not measured | Only Grok 4.7 measured |
| Tax Agent Bench (Vals AI) | 34.2% | Not measured | Only Grok 4.7 measured |
| Legal Research Bench (Vals AI) | 47.1% | Not measured | Only Grok 4.7 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 18.3% | Not measured | Only Grok 4.7 measured |
| Excel Modeling Benchmark (Vals AI) | 67.0% | Not measured | Only Grok 4.7 measured |
| BioMysteryBench (Vals AI) | 69.3% | Not measured | Only Grok 4.7 measured |
| Terminal-Bench-Science (Vals AI) | 10.0% | Not measured | Only Grok 4.7 measured |
| Vals Science Index | 34.8% | Not measured | Only Grok 4.7 measured |
| Robo-bench (Vals AI) | 0.2% | Not measured | Only Grok 4.7 measured |
| τ²-Bench Telecom | Not measured | 89.2% | Only Qwen3.5-35B-A3B measured |
| APEX-Agents | 54.6% | Not measured | Only Grok 4.7 measured |
| Vending-Bench 2 | $10,537 | $463 | Grok 4.7 |
| Neither model has a result on the other 23 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | Grok 4.7 | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| LiveBench reasoning | 82.7% | Not measured | Only Grok 4.7 measured |
| MysteryMechanism (Vals AI) | 25.2% | Not measured | Only Grok 4.7 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 84.5% | Only Qwen3.5-35B-A3B measured |
| Humanity's Last Exam (Artificial Analysis run) | 43.1% | 21.0% | Grok 4.7 |
| GPQA Diamond (Epoch AI run) | 92.7% | 83.5% | Grok 4.7 |
| ARC-AGI | 90.2% | Not measured | Only Grok 4.7 measured |
| ARC-AGI-2 | 61.4% | Not measured | Only Grok 4.7 measured |
| Chess puzzles | 41.0% | Not measured | Only Grok 4.7 measured |
| Mystery game puzzles | 29.0% | Not measured | Only Grok 4.7 measured |
| LMArena Text: hard prompts | 1415 | 1400 | Grok 4.7 |
| LMArena Text: expert | 1439 | 1408 | Grok 4.7 |
| LMArena Text: hard prompts in English | 1427 | 1411 | Grok 4.7 |
| LMArena Vision: diagrams | 1254 | Not measured | Only Grok 4.7 measured |
| LMArena Vision: homework | 1309 | Not measured | Only Grok 4.7 measured |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Grok 4.7 | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| OTIS Mock AIME | 98.1% | 70.0% | Grok 4.7 |
| FrontierMath, tiers 1 to 3 | 53.0% | Not measured | Only Grok 4.7 measured |
| FrontierMath, tier 4 | 17.1% | Not measured | Only Grok 4.7 measured |
| ProofBench | 26.0% | Not measured | Only Grok 4.7 measured |
| LMArena Text: math | 1414 | 1404 | Grok 4.7 |
| LMArena Text: mathematical work | 1435 | 1410 | Grok 4.7 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index22
| Benchmark | Grok 4.7 | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | 84.4% | Not measured | Only Grok 4.7 measured |
| MedCode (Vals AI) | 49.5% | Not measured | Only Grok 4.7 measured |
| SimpleQA Verified | 56.0% | Not measured | Only Grok 4.7 measured |
| LMArena Text: science | 1419 | 1413 | Grok 4.7 |
| LMArena Text: medicine and healthcare | 1395 | 1421 | Qwen3.5-35B-A3B |
| LMArena Text: legal and government | 1423 | 1399 | Grok 4.7 |
| LMArena Text: business and finance | 1389 | 1394 | Qwen3.5-35B-A3B |
| Neither model has a result on the other 10 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Grok 4.7 | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| Design Arena: websites | 1255 | Not measured | Only Grok 4.7 measured |
| Design Arena: UI components | 1271 | Not measured | Only Grok 4.7 measured |
| Design Arena: game dev | 1314 | Not measured | Only Grok 4.7 measured |
| Design Arena: data viz | 1227 | Not measured | Only Grok 4.7 measured |
| Design Arena: 3D | 1269 | Not measured | Only Grok 4.7 measured |
| Design Arena: all code categories | 1271 | Not measured | Only Grok 4.7 measured |
| LiveBench design | 78.0% | Not measured | Only Grok 4.7 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index182
| Benchmark | Grok 4.7 | Qwen3.5-35B-A3B | Leader |
|---|---|---|---|
| Vals Index | 58.6% | Not measured | Only Grok 4.7 measured |
| LiveBench data analysis | 76.9% | Not measured | Only Grok 4.7 measured |
| Public Benefits Bench (Vals AI) | 65.6% | Not measured | Only Grok 4.7 measured |
| SAGE (Vals AI) | 40.8% | Not measured | Only Grok 4.7 measured |
| MedScribe (Vals AI) | 89.4% | Not measured | Only Grok 4.7 measured |
| IFBench | Not measured | 72.5% | Only Qwen3.5-35B-A3B measured |
| Long context reasoning (AA-LCR) | 80.0% | 72.0% | Grok 4.7 |
| DTBench | 96.0% | 80.0% | Grok 4.7 |
| LMCA | 49.4% | 29.5% | Grok 4.7 |
| Furniture assembly | 20.8% | Not measured | Only Grok 4.7 measured |
| LMArena Text | 1399 | 1395 | Grok 4.7 |
| LMArena Text: instruction following | 1406 | 1379 | Grok 4.7 |
| LMArena Text: creative writing | 1381 | 1347 | Grok 4.7 |
| LMArena Vision | 1231 | Not measured | Only Grok 4.7 measured |
| LMArena Text: English | 1411 | 1407 | Grok 4.7 |
| LMArena Text: non-English | 1386 | 1378 | Grok 4.7 |
| LMArena Text: Chinese | 1449 | 1457 | Qwen3.5-35B-A3B |
| LMArena Text: German | 1366 | 1367 | Qwen3.5-35B-A3B |
| LMArena Text: Japanese | 1359 | 1326 | Grok 4.7 |
| LMArena Text: Korean | 1383 | 1355 | Grok 4.7 |
| LMArena Text: French | 1431 | 1412 | Grok 4.7 |
| LMArena Text: Spanish | 1406 | 1390 | Grok 4.7 |
| LMArena Text: Russian | 1399 | 1376 | Grok 4.7 |
| LMArena Text: Polish | 1420 | 1385 | Grok 4.7 |
| LMArena Text: multi-turn | 1394 | 1390 | Grok 4.7 |
| LMArena Text: longer queries | 1416 | 1389 | Grok 4.7 |
| LMArena Text: writing and language | 1394 | 1365 | Grok 4.7 |
| LMArena Text: entertainment and media | 1376 | 1347 | Grok 4.7 |
| LMArena Vision: English | 1229 | Not measured | Only Grok 4.7 measured |
| LMArena Vision: Chinese | 1250 | Not measured | Only Grok 4.7 measured |
| LMArena Vision: reading text | 1241 | Not measured | Only Grok 4.7 measured |
| LMArena Vision: creative writing | 1217 | Not measured | Only Grok 4.7 measured |
| Neither model has a result on the other 14 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Grok 4.7 | 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 | SpaceXAI Grok 4.7 | Qwen3.5-35B-A3B |
|---|---|---|
| Maker | xAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | September 21, 2026 | February 25, 2026 |
| Context window | 500K | 262K |
| Input $/Mtok | $2.00 | $0.14 |
| Output $/Mtok | $6.00 | $1.00 |
| Coding index | 75.0 | 7.7 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Grok 4.7 better than Qwen3.5-35B-A3B for coding?
Pick Grok 4.7 for coding: it scores 75.0 on the Vibe Coding Index against 7.7 for Qwen3.5-35B-A3B. Pick Qwen3.5-35B-A3B when output tokens cost 6.0x less, or when what matters is LMArena Text: medicine and healthcare or LMArena Text: business and finance, where it leads.
Which is cheaper, Grok 4.7 or Qwen3.5-35B-A3B?
Grok 4.7 costs $2.00 per million input tokens and $6.00 per million output tokens; Qwen3.5-35B-A3B costs $0.14 and $1.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Grok 4.7 or Qwen3.5-35B-A3B?
Grok 4.7, with 500K tokens against 262K.
How many benchmarks is the Grok 4.7 vs Qwen3.5-35B-A3B comparison based on?
40 benchmarks have a result for both models as of October 21, 2026. Grok 4.7's coding index rests on 2 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.