| Signal | Llama 3.2 1B Instruct | Delta | Qwen3.6 27B |
|---|---|---|---|
Capabilities | 17 | -67 | |
Benchmarks | 19 | +19 | |
Pricing | 100 | +3 | |
Context window size | 73 | -4 | |
Recency | 17 | -83 | |
Output Capacity | 80 | -11 | |
| Overall Result | 2 wins | of 6 | 4 wins |
Score History
17.6
current score
Qwen3.6 27B
right now
40
current score
Meta
Alibaba
Llama 3.2 1B Instruct saves you $174.60/month
That's $2095.20/year compared to Qwen3.6 27B at your current usage level of 100K calls/month.
| Metric | Llama 3.2 1B Instruct | Qwen3.6 27B | Winner |
|---|---|---|---|
| Overall Score | 18 | 40 | Qwen3.6 27B |
| Rank | #315 | #198 | Qwen3.6 27B |
| Quality Rank | #315 | #198 | Qwen3.6 27B |
| Adoption Rank | #315 | #198 | Qwen3.6 27B |
| Parameters | 1B | 27B | -- |
| Context Window | 131K | 262K | Qwen3.6 27B |
| Pricing | $0.03/$0.20/M | $0.29/$3.17/M | -- |
| Signal Scores | |||
| Capabilities | 17 | 83 | Qwen3.6 27B |
| Benchmarks | 19 | -- | Llama 3.2 1B Instruct |
| Pricing | 100 | 97 | Llama 3.2 1B Instruct |
| Context window size | 73 | 77 | Qwen3.6 27B |
| Recency | 17 | 100 | Qwen3.6 27B |
| Output Capacity | 80 | 90 | Qwen3.6 27B |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.
Scores 18/100 (rank #315), placing it in the top -8% of all 290 models tracked.
Scores 40/100 (rank #198), placing it in the top 32% of all 290 models tracked.
Qwen3.6 27B has a 22-point advantage, which typically translates to noticeably stronger performance on complex reasoning, code generation, and multi-step tasks.
Llama 3.2 1B Instruct offers 93% better value per quality point. At 1M tokens/day, you'd spend $3.42/month with Llama 3.2 1B Instruct vs $51.88/month with Qwen3.6 27B - a $48.46 monthly difference.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Suitable for user-facing chat with competitive response times. Llama 3.2 1B Instruct also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (262K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.20/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (40/100) correlates with better nuance, coherence, and style in long-form content
Image understanding & OCR
Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly
Qwen3.6 27B clearly outperforms Llama 3.2 1B Instruct with a significant 22.4-point lead. For most general use cases, Qwen3.6 27B is the stronger choice. However, Llama 3.2 1B Instruct may still excel in niche scenarios.
Best for Quality
Llama 3.2 1B Instruct
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 3.2 1B Instruct
93% lower pricing; better value at scale
Best for Reliability
Llama 3.2 1B Instruct
Higher uptime and faster response speeds
Best for Prototyping
Llama 3.2 1B Instruct
Stronger community support and better developer experience
Best for Production
Llama 3.2 1B Instruct
Wider enterprise adoption and proven at scale
by Meta
| Capability | Llama 3.2 1B Instruct | Qwen3.6 27B |
|---|---|---|
| Vision (Image Input)differs | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Meta
Alibaba
Llama 3.2 1B Instruct saves you $4.03/month
That's 93% cheaper than Qwen3.6 27B at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | Llama 3.2 1B Instruct | Qwen3.6 27B |
|---|---|---|
| Context Window | 131K | 262K |
| Max Output Tokens | 60,000 | 262,140 |
| Open Source | Yes | Yes |
| Created | Sep 25, 2024 | Apr 27, 2026 |