Compare / Qwen3.5-27B vs Llama 3.2 1B Instruct
Qwen3.5-27B vs Llama 3.2 1B Instruct
Which is cheaper, which is more capable, and which does better at each kind of work, from 16 results on 3 sources that measured both models.
Qwen3.5-27B or Llama 3.2 1B Instruct? The short answer
- Llama 3.2 1B Instruct is cheaper: $0.071 against $0.74 per million tokens, 11 times less.
- Qwen3.5-27B scores higher on the Artificial Analysis Intelligence Index: 22.9 against 4.8.
- On our business benchmarks, Qwen3.5-27B ranks higher for agents and tool use, reasoning and knowledge and writing.
At a glance
| Qwen3.5-27B | Llama 3.2 1B Instruct | |
|---|---|---|
| Price per million tokens | $0.27 in · $2.16 out | $0.027 in · $0.20 out |
| Blended (3 in : 1 out) | $0.74 | $0.071 |
| Cheapest host, blended | $0.54 (Alibaba) | – |
| Intelligence Index | 22.9 | 4.8 |
| Spring Prompt overall | – | – |
| Output speed, tokens a second | – | – |
| Context window | 262,144 tokens | 60,000 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 | Qwen3.5-27B | Llama 3.2 1B Instruct |
|---|---|---|
| Agents and tool useMulti-step tasks with tools: support desks, coding agents, function calls | 144 of 161 | 161 of 161 |
| Reasoning and knowledgeHard questions across science, maths and general knowledge | 78 of 195 | 189 of 195 |
| WritingWhat people prefer in blind comparisons, and judged writing quality | 95 of 187 | 186 of 187 |
| Sticking to the factsSummarising without inventing things, and factual answers | 122 of 153 | not measured |
Every shared result
| Benchmark | Metric | Qwen3.5-27B | Llama 3.2 1B Instruct |
|---|---|---|---|
| Arena (formerly LMArena)reported | Business, management and financeArena rating | 1,408 | 1,070 |
| Arena (formerly LMArena)reported | Creative writingArena rating | 1,358 | 1,083 |
| Arena (formerly LMArena)reported | Expert promptsArena rating | 1,441 | 1,082 |
| Arena (formerly LMArena)reported | Instruction followingArena rating | 1,400 | 1,086 |
| Arena (formerly LMArena)reported | OverallArena rating | 1,409 | 1,111 |
| Arena (formerly LMArena)reported | Writing, literature and languageArena rating | 1,380 | 1,076 |
| Artificial Analysisreported | Artificial Analysis Intelligence Indexindex score | 22.9 | 4.80 |
| Artificial Analysisreported | GPQA Diamond% of questions | 85.8% | 19.6% |
| Artificial Analysisreported | Humanity's Last Exam% of questions | 23.9% | 5.5% |
| Artificial Analysisreported | IFBench% of instructions | 75.6% | 22.8% |
| Artificial Analysisreported | Long-context reasoning (AA-LCR)% of questions | 77.7% | 6.7% |
| Artificial Analysisreported | Terminal-Bench Hard% of tasks | 32.6% | 0.0% |
| Artificial Analysisreported | Τ²-bench telecom% of tasks | 93.9% | 0.0% |
| UGI Leaderboardreported | Requested-length error% off the requested word count | 11.0% | 22.0% |
| UGI Leaderboardreported | Style adherencescore from 0 to 1 | 0.33 | 0.33 |
| UGI Leaderboardreported | Writing scorescore out of 100 | 42.4 | 11.8 |
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 Qwen3.5-27B better than Llama 3.2 1B Instruct?
It depends on the task. Among models available today, on our benchmarks and licensed sources, Qwen3.5-27B ranks higher for agents and tool use, reasoning and knowledge and writing.
Which is cheaper, Qwen3.5-27B or Llama 3.2 1B Instruct?
Llama 3.2 1B Instruct costs $0.071 per million tokens (three input to one output), against $0.74 for Qwen3.5-27B.
Run Qwen3.5-27B and Llama 3.2 1B Instruct on your own prompt
Benchmarks aren't your data. Try both side by side in the playground, with the cost of every answer.