Model comparison
Llama-3.3-70B-Instruct vs Qwen3.5-Flash
Qwen3.5-Flash is the stronger model overall, scoring 42.3 to 30.3 on the Noometry Index.
Last verified . 23 shared benchmarks.
Meta
Alibaba (Qwen)Summary
- They share 23 benchmarks with published results for both. Llama-3.3-70B-Instruct 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 14.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 5.1% for Llama-3.3-70B-Instruct and 84.4% for Qwen3.5-Flash.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $0.10 / $0.40 for Qwen3.5-Flash.
- Qwen3.5-Flash accepts more context: 1M tokens versus 128K.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.
Side by side
| Llama-3.3-70B-Instruct | Qwen3.5-Flash | |
|---|---|---|
| Provider | Meta | Alibaba (Qwen) |
| Noometry Index | 30.3 | 42.3 |
| Released | 2024-12-06 | 2026-02-23 |
| Weights | Open | Proprietary |
| Context window | 128K | 1M |
| Max output | 4K | 66K |
| Input $ / M tokens | $0.10 | $0.10 |
| Output $ / M tokens | $0.32 | $0.40 |
| Results tracked | 43 | 32 |
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Category by category
Coding Qwen3.5-Flash leads
Llama-3.3-70B-Instruct: 30.6 (#284), Qwen3.5-Flash: 33.9 (#234)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.5-Flash |
|---|---|---|
| LMArena Coding | 1268 | 1412 |
| LMArena WebDev | — | 1243 |
| SciCode | 26% | — |
| WeirdML | 14.4% | — |
| BigCodeBench Instruct | 46.9% | — |
| LiveBench Coding | 36.6% | — |
| BigCodeBench Complete | 57.5% | — |
| ALE-Bench | — | 221.8 |
Agentic & Tool Use Not comparable
Llama-3.3-70B-Instruct: 25.6 (#100), Qwen3.5-Flash: —
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.5-Flash |
|---|---|---|
| Berkeley Function Calling Leaderboard | 31.9% | — |
| BALROG | 23% | — |
| Vending-Bench 2 | — | 462.69 |
Reasoning Qwen3.5-Flash leads
Llama-3.3-70B-Instruct: 13.7 (#325), Qwen3.5-Flash: 32.9 (#63)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.5-Flash |
|---|---|---|
| LMArena Hard Prompts | 1257 | 1404 |
| DTBench | 59.5% | 82.9% |
| LMCA | 17.5% | 29.1% |
| Epoch Capabilities Index | 127.32 | 143.98 |
| SimpleBench | 19.9% | — |
| CritPt | 0% | — |
| Chess Puzzles | — | 21% |
| LiveBench Reasoning | 50.8% | — |
| Mystery Game Puzzles | — | 20% |
| LiveBench Data Analysis | 49.5% | — |
| ForecastBench | 58.6 | — |
| LiveBench | 50.2% | — |
Math Qwen3.5-Flash leads
Llama-3.3-70B-Instruct: 14.3 (#292), Qwen3.5-Flash: 36.3 (#117)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.5-Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 5.1% | 84.4% |
| LMArena Math | 1268 | 1407 |
| FrontierMath (Tiers 1-3) | — | 18.2% |
| LiveBench Math | 42.2% | — |
| MATH Level 5 | 41.6% | — |
| FrontierMath (Feb 2025 set) | — | 6.2% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Qwen3.5-Flash leads
Llama-3.3-70B-Instruct: 30.5 (#220), Qwen3.5-Flash: 43.2 (#84)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.5-Flash |
|---|---|---|
| GPQA Diamond | 47.4% | 82.3% |
| Vectara Hallucination Rate | 4.1% | 10.5% |
| LMArena Expert | 1225 | 1408 |
| SimpleQA Verified | — | 20.3% |
| Confabulations | 22.8% | — |
| MMLU | 86.3% | — |
Multilingual Qwen3.5-Flash leads
Llama-3.3-70B-Instruct: 40.1 (#215), Qwen3.5-Flash: 50.7 (#116)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.5-Flash |
|---|---|---|
| LMArena Non-English | 1236 | 1385 |
| LMArena Chinese | 1217 | 1446 |
| LMArena French | 1280 | 1412 |
| LMArena German | 1251 | 1389 |
| LMArena Japanese | 1149 | 1369 |
| LMArena Korean | 1142 | 1344 |
| LMArena Russian | 1252 | 1379 |
| LMArena Spanish | 1269 | 1399 |
Instruction Following Qwen3.5-Flash leads
Llama-3.3-70B-Instruct: 71.3 (#152), Qwen3.5-Flash: 72.8 (#133)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.5-Flash |
|---|---|---|
| LMArena Instruction Following | 1242 | 1374 |
| LiveBench Instruction Following | 82.7% | — |
Long Context Qwen3.5-Flash leads
Llama-3.3-70B-Instruct: 26.4 (#290), Qwen3.5-Flash: 42.4 (#119)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.5-Flash |
|---|---|---|
| LMArena Longer Query | 1256 | 1392 |
| Fiction.LiveBench | 33.3% | — |
Writing & Preference Qwen3.5-Flash leads
Llama-3.3-70B-Instruct: 47.8 (#203), Qwen3.5-Flash: 58.2 (#117)
| Benchmark | Llama-3.3-70B-Instruct | Qwen3.5-Flash |
|---|---|---|
| LMArena Text | 1274 | 1397 |
| LMArena Creative Writing | 1250 | 1343 |
| LMArena Multi-Turn | 1279 | 1393 |
| LiveBench Language | 39.2% | — |
Frequently asked questions
Is Llama-3.3-70B-Instruct better than Qwen3.5-Flash?
Qwen3.5-Flash is the stronger model overall, scoring 42.3 to 30.3 on the Noometry Index.
Which is cheaper, Llama-3.3-70B-Instruct or Qwen3.5-Flash?
Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; Qwen3.5-Flash lists at $0.10 and $0.40.
Is Llama-3.3-70B-Instruct or Qwen3.5-Flash better for coding?
Qwen3.5-Flash scores higher on coding benchmarks: 33.9 versus 30.6 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-3.3-70B-Instruct and Qwen3.5-Flash share?
23 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and Qwen3.5-Flash has 32.