Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Grok Code Fast 1
56
1/8 categoriesQwen3.5-122B-A10B
71
Winner · 6/8 categoriesGrok Code Fast 1· Qwen3.5-122B-A10B
Pick Qwen3.5-122B-A10B if you want the stronger benchmark profile. Grok Code Fast 1 only becomes the better choice if reasoning is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.5-122B-A10B is clearly ahead on the aggregate, 71 to 56. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5-122B-A10B's sharpest advantage is in knowledge, where it averages 81.6 against 49. The single biggest benchmark swing on the page is MMMU-Pro, 40% to 76.9%. Grok Code Fast 1 does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
Qwen3.5-122B-A10B is the reasoning model in the pair, while Grok Code Fast 1 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Qwen3.5-122B-A10B gives you the larger context window at 262K, compared with 256K for Grok Code Fast 1.
BenchLM keeps the benchmark table and the operator tradeoffs on the same page so a better score does not hide a materially slower, pricier, or smaller-context model.
Runtime metrics show N/A when BenchLM does not have a sourced snapshot for that exact model. The scoring rules and freshness policy are documented on the methodology page.
| Benchmark | Grok Code Fast 1 | Qwen3.5-122B-A10B |
|---|---|---|
| AgenticQwen3.5-122B-A10B wins | ||
| Terminal-Bench 2.0 | 59% | 49.4% |
| BrowseComp | 57% | 63.8% |
| OSWorld-Verified | 51% | 58% |
| tau2-bench | — | 79.5% |
| CodingQwen3.5-122B-A10B wins | ||
| HumanEval | 60% | — |
| SWE-bench Verified | 70.8% | 72% |
| LiveCodeBench | 80% | 78.9% |
| SWE-bench Pro | 42% | — |
| Multimodal & GroundedQwen3.5-122B-A10B wins | ||
| MMMU-Pro | 40% | 76.9% |
| OfficeQA Pro | 63% | — |
| ReasoningGrok Code Fast 1 wins | ||
| MuSR | 59% | — |
| BBH | 75% | — |
| LongBench v2 | 64% | 60.2% |
| MRCRv2 | 66% | — |
| KnowledgeQwen3.5-122B-A10B wins | ||
| MMLU | 64% | — |
| GPQA | 63% | 86.6% |
| SuperGPQA | 61% | 67.1% |
| MMLU-Pro | 65% | 86.7% |
| HLE | 7% | — |
| FrontierScience | 57% | — |
| SimpleQA | 61% | — |
| Instruction FollowingQwen3.5-122B-A10B wins | ||
| IFEval | 79% | 93.4% |
| MultilingualQwen3.5-122B-A10B wins | ||
| MGSM | 75% | — |
| MMLU-ProX | 73% | 82.2% |
| Mathematics | ||
| AIME 2023 | 64% | — |
| AIME 2024 | 66% | — |
| AIME 2025 | 65% | — |
| HMMT Feb 2023 | 60% | — |
| HMMT Feb 2024 | 62% | — |
| HMMT Feb 2025 | 61% | — |
| BRUMO 2025 | 63% | — |
| MATH-500 | 73% | — |
Qwen3.5-122B-A10B is ahead overall, 71 to 56. The biggest single separator in this matchup is MMMU-Pro, where the scores are 40% and 76.9%.
Qwen3.5-122B-A10B has the edge for knowledge tasks in this comparison, averaging 81.6 versus 49. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for coding in this comparison, averaging 76.3 versus 63.3. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Grok Code Fast 1 has the edge for reasoning in this comparison, averaging 63.3 versus 60.2. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for agentic tasks in this comparison, averaging 56 versus 55.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for multimodal and grounded tasks in this comparison, averaging 76.9 versus 50.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for instruction following in this comparison, averaging 93.4 versus 79. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Qwen3.5-122B-A10B has the edge for multilingual tasks in this comparison, averaging 82.2 versus 73.7. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.