Model comparison
Llama 3.1-70B vs Qwen3.5-Flash
Qwen3.5-Flash is the stronger model overall, scoring 42.3 to 29.4 on the Noometry Index.
Last verified . 22 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 22 benchmarks with published results for both. Llama 3.1-70B scores higher in 0 categories and Qwen3.5-Flash in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.5-Flash leads 36.3 to 12.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 3.6% for Llama 3.1-70B and 84.4% for Qwen3.5-Flash.
- Qwen3.5-Flash is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.40 / $0.40 for Llama 3.1-70B.
- Qwen3.5-Flash accepts more context: 1M tokens versus 128K.
- Llama 3.1-70B has downloadable open weights; the other is API-only.
Side by side
| Llama 3.1-70B | Qwen3.5-Flash | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 29.4 | 42.3 |
| Released | 2024-07-23 | 2026-02-23 |
| Weights | Open | Proprietary |
| Context window | 128K | 1M |
| Max output | 4K | 66K |
| Input $ / M tokens | $0.40 | $0.10 |
| Output $ / M tokens | $0.40 | $0.40 |
| Results tracked | 35 | 32 |
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Category by category
Coding Qwen3.5-Flash leads
Llama 3.1-70B: 29.8 (#292), Qwen3.5-Flash: 33.9 (#234)
| Benchmark | Llama 3.1-70B | Qwen3.5-Flash |
|---|---|---|
| LMArena Coding | 1260 | 1412 |
| LMArena WebDev | — | 1243 |
| WeirdML | 9% | — |
| BigCodeBench Instruct | 46.1% | — |
| BigCodeBench Complete | 54.8% | — |
| ALE-Bench | — | 221.8 |
Agentic & Tool Use Not comparable
Llama 3.1-70B: 24.9 (#104), Qwen3.5-Flash: —
| Benchmark | Llama 3.1-70B | Qwen3.5-Flash |
|---|---|---|
| TheAgentCompany | 6.9% | — |
| BALROG | 27.9% | — |
| Vending-Bench 2 | — | 462.69 |
Reasoning Qwen3.5-Flash leads
Llama 3.1-70B: 20.8 (#215), Qwen3.5-Flash: 32.9 (#63)
| Benchmark | Llama 3.1-70B | Qwen3.5-Flash |
|---|---|---|
| LMArena Hard Prompts | 1241 | 1404 |
| DTBench | 60% | 82.9% |
| LMCA | 14.8% | 29.1% |
| Epoch Capabilities Index | 125.91 | 143.98 |
| Chess Puzzles | — | 21% |
| Mystery Game Puzzles | — | 20% |
Math Qwen3.5-Flash leads
Llama 3.1-70B: 12.7 (#298), Qwen3.5-Flash: 36.3 (#117)
| Benchmark | Llama 3.1-70B | Qwen3.5-Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 3.6% | 84.4% |
| LMArena Math | 1252 | 1407 |
| FrontierMath (Tiers 1-3) | — | 18.2% |
| Omni-MATH | 21% | — |
| MATH Level 5 | 36.7% | — |
| FrontierMath (Feb 2025 set) | — | 6.2% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Qwen3.5-Flash leads
Llama 3.1-70B: 24.1 (#264), Qwen3.5-Flash: 43.2 (#84)
| Benchmark | Llama 3.1-70B | Qwen3.5-Flash |
|---|---|---|
| GPQA Diamond | 44.2% | 82.3% |
| LMArena Expert | 1209 | 1408 |
| SimpleQA Verified | — | 20.3% |
| MMLU-Pro | 65.3% | — |
| Vectara Hallucination Rate | — | 10.5% |
| GPQA (HELM) | 42.6% | — |
| MMLU | 80.1% | — |
Multilingual Qwen3.5-Flash leads
Llama 3.1-70B: 38.9 (#220), Qwen3.5-Flash: 50.7 (#116)
| Benchmark | Llama 3.1-70B | Qwen3.5-Flash |
|---|---|---|
| LMArena Non-English | 1219 | 1385 |
| LMArena Chinese | 1215 | 1446 |
| LMArena French | 1260 | 1412 |
| LMArena German | 1222 | 1389 |
| LMArena Japanese | 1132 | 1369 |
| LMArena Korean | 1140 | 1344 |
| LMArena Russian | 1234 | 1379 |
| LMArena Spanish | 1253 | 1399 |
Instruction Following Qwen3.5-Flash leads
Llama 3.1-70B: 65.5 (#218), Qwen3.5-Flash: 72.8 (#133)
| Benchmark | Llama 3.1-70B | Qwen3.5-Flash |
|---|---|---|
| LMArena Instruction Following | 1231 | 1374 |
| IFEval | 82.1% | — |
Long Context Qwen3.5-Flash leads
Llama 3.1-70B: 37.6 (#208), Qwen3.5-Flash: 42.4 (#119)
| Benchmark | Llama 3.1-70B | Qwen3.5-Flash |
|---|---|---|
| LMArena Longer Query | 1241 | 1392 |
Writing & Preference Qwen3.5-Flash leads
Llama 3.1-70B: 35.6 (#262), Qwen3.5-Flash: 58.2 (#117)
| Benchmark | Llama 3.1-70B | Qwen3.5-Flash |
|---|---|---|
| LMArena Text | 1261 | 1397 |
| LMArena Creative Writing | 1232 | 1343 |
| LMArena Multi-Turn | 1256 | 1393 |
| EQ-Bench Creative Writing | 784 | — |
| WildBench | 75.8% | — |
Frequently asked questions
Is Llama 3.1-70B better than Qwen3.5-Flash?
Qwen3.5-Flash is the stronger model overall, scoring 42.3 to 29.4 on the Noometry Index.
Which is cheaper, Llama 3.1-70B or Qwen3.5-Flash?
Qwen3.5-Flash is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; Llama 3.1-70B lists at $0.40 and $0.40.
Is Llama 3.1-70B or Qwen3.5-Flash better for coding?
Qwen3.5-Flash scores higher on coding benchmarks: 33.9 versus 29.8 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5-Flash does, with 1M tokens against 128K.
How many benchmarks do Llama 3.1-70B and Qwen3.5-Flash share?
22 benchmarks have published results for both models. Llama 3.1-70B has 35 scored results on Noometry and Qwen3.5-Flash has 32.