Compare / Qwen2.5 72B Instruct vs Llama 3.3 70B Instruct
Qwen2.5 72B Instruct vs Llama 3.3 70B Instruct
Which is cheaper, which is more capable, and which does better at each kind of work, from 15 results on 3 sources that measured both models.
Qwen2.5 72B Instruct or Llama 3.3 70B Instruct? The short answer
- Qwen2.5 72B Instruct is cheaper: $0.37 against $0.64 per million tokens, 42% less.
- They score about the same on the Artificial Analysis Intelligence Index (7.7 and 7.7).
- On our business benchmarks, Qwen2.5 72B Instruct ranks higher for writing.
- On our business benchmarks, Llama 3.3 70B Instruct ranks higher for reasoning and knowledge.
At a glance
| Qwen2.5 72B Instruct | Llama 3.3 70B Instruct | |
|---|---|---|
| Price per million tokens | $0.36 in · $0.40 out | $0.59 in · $0.79 out |
| Blended (3 in : 1 out) | $0.37 | $0.64 |
| Cheapest host, blended | $0.37 (DeepInfra, FP8) | $0.16 (DeepInfra, FP8) |
| Intelligence Index | 7.7 | 7.7 |
| Spring Prompt overall | – | – |
| Output speed, tokens a second | – | 80 |
| Context window | 32,768 tokens | 131,072 tokens |
| Weights | Open | Open |
| Developer based in | China | the US |
Prices are the developer's list price where we have read it, otherwise the typical host on OpenRouter; see every model's price. Intelligence and speed from Artificial Analysis.
Which is better for what
Each model's rank among models available today, for each kind of work, from our own benchmarks and licensed sources. The better rank is in green.
| Use case | Qwen2.5 72B Instruct | Llama 3.3 70B Instruct |
|---|---|---|
| Agents and tool useMulti-step tasks with tools: support desks, coding agents, function calls | not measured | 127 of 161 |
| Reasoning and knowledgeHard questions across science, maths and general knowledge | 175 of 195 | 174 of 195 |
| WritingWhat people prefer in blind comparisons, and judged writing quality | 159 of 187 | 165 of 187 |
Every shared result
| Benchmark | Metric | Qwen2.5 72B Instruct | Llama 3.3 70B Instruct |
|---|---|---|---|
| Arena (formerly LMArena)reported | Business, management and financeArena rating | 1,299 | 1,304 |
| Arena (formerly LMArena)reported | Creative writingArena rating | 1,254 | 1,285 |
| Arena (formerly LMArena)reported | Expert promptsArena rating | 1,295 | 1,298 |
| Arena (formerly LMArena)reported | Instruction followingArena rating | 1,292 | 1,293 |
| Arena (formerly LMArena)reported | OverallArena rating | 1,303 | 1,318 |
| Arena (formerly LMArena)reported | Writing, literature and languageArena rating | 1,282 | 1,292 |
| Artificial Analysisreported | Artificial Analysis Intelligence Indexindex score | 7.70 | 7.70 |
| Artificial Analysisreported | GPQA Diamond% of questions | 49.1% | 49.8% |
| Artificial Analysisreported | Humanity's Last Exam% of questions | 3.6% | 3.6% |
| Artificial Analysisreported | IFBench% of instructions | 36.9% | 47.1% |
| Artificial Analysisreported | Terminal-Bench Hard% of tasks | 4.5% | 3.0% |
| Artificial Analysisreported | Τ²-bench telecom% of tasks | 34.5% | 26.6% |
| UGI Leaderboardreported | Requested-length error% off the requested word count | 21.0% | 10.0% |
| UGI Leaderboardreported | Style adherencescore from 0 to 1 | 0.36 | 0.37 |
| UGI Leaderboardreported | Writing scorescore out of 100 | 31.5 | 26.2 |
Bold green marks the better value on that metric. Where a source reports ranges that overlap, the difference may not be meaningful; see the benchmark page for ranges.
Quick answers
Is Qwen2.5 72B Instruct better than Llama 3.3 70B Instruct?
It depends on the task. Among models available today, on our benchmarks and licensed sources, Qwen2.5 72B Instruct ranks higher for writing; Llama 3.3 70B Instruct ranks higher for reasoning and knowledge.
Which is cheaper, Qwen2.5 72B Instruct or Llama 3.3 70B Instruct?
Qwen2.5 72B Instruct costs $0.37 per million tokens (three input to one output), against $0.64 for Llama 3.3 70B Instruct.
Run Qwen2.5 72B Instruct and Llama 3.3 70B Instruct on your own prompt
Benchmarks aren't your data. Try both side by side in the playground, with the cost of every answer.