| Signal | Llama 3 70B Instruct | Delta | QwQ 32B |
|---|---|---|---|
Capabilities | 17 | -33 | |
Benchmarks | 61 | +3 | |
Pricing | 99 | 0 | |
Context window size | 62 | -19 | |
Recency | 2 | -58 | |
Output Capacity | 65 | -20 | |
| Overall Result | 1 wins | of 6 | 5 wins |
Score History
58.9
current score
Tied
right now
58.9
current score
Meta
Alibaba
QwQ 32B saves you $44.00/month
That's $528.00/year compared to Llama 3 70B Instruct at your current usage level of 100K calls/month.
| Metric | Llama 3 70B Instruct | QwQ 32B | Winner |
|---|---|---|---|
| Overall Score | 59 | 59 | -- |
| Rank | #136 | #135 | QwQ 32B |
| Quality Rank | #136 | #135 | QwQ 32B |
| Adoption Rank | #136 | #135 | QwQ 32B |
| Parameters | 70B | 32B | -- |
| Context Window | 8K | 131K | QwQ 32B |
| Pricing | $0.51/$0.74/M | $0.15/$0.58/M | -- |
| Signal Scores | |||
| Capabilities | 17 | 50 | QwQ 32B |
| Benchmarks | 61 | 58 | Llama 3 70B Instruct |
| Pricing | 99 | 99 | QwQ 32B |
| Context window size | 62 | 81 | QwQ 32B |
| Recency | 2 | 61 | QwQ 32B |
| Output Capacity | 65 | 85 | QwQ 32B |
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 59/100 (rank #136), placing it in the top 53% of all 290 models tracked.
Scores 59/100 (rank #135), placing it in the top 54% of all 290 models tracked.
With only a 0-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.
QwQ 32B offers 42% better value per quality point. At 1M tokens/day, you'd spend $10.95/month with QwQ 32B vs $18.75/month with Llama 3 70B Instruct - a $7.80 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
Higher benchmark score (0/100) indicates stronger performance on coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Faster response time (speed score 0/100) is critical for user-facing chat. QwQ 32B also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (131K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.58/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (59/100) correlates with better nuance, coherence, and style in long-form content
Llama 3 70B Instruct and QwQ 32B are extremely close in overall performance (only 0 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Llama 3 70B Instruct
Marginally better benchmark scores; both are excellent
Best for Cost
QwQ 32B
42% lower pricing; better value at scale
Best for Reliability
Llama 3 70B Instruct
Higher uptime and faster response speeds
Best for Prototyping
Llama 3 70B Instruct
Stronger community support and better developer experience
Best for Production
Llama 3 70B Instruct
Wider enterprise adoption and proven at scale
by Meta
| Capability | Llama 3 70B Instruct | QwQ 32B |
|---|---|---|
| Vision (Image Input) | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Meta
Alibaba
QwQ 32B saves you $0.8400/month
That's 47% cheaper than Llama 3 70B Instruct 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 70B Instruct | QwQ 32B |
|---|---|---|
| Context Window | 8K | 131K |
| Max Output Tokens | 8,000 | 131,072 |
| Open Source | Yes | Yes |
| Created | Apr 18, 2024 | Mar 5, 2025 |
Both Llama 3 70B Instruct and QwQ 32B score 59/100, making them extremely close competitors. Choose based on pricing, provider ecosystem, or specific capability requirements.
Llama 3 70B Instruct is ranked #136 and QwQ 32B is ranked #135 out of 290+ AI models. Rankings use a composite score combining benchmark performance (90%) from MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations, with capabilities and context window as tiebreakers (10%). Scores update hourly.
QwQ 32B is cheaper at $0.58/M output tokens vs Llama 3 70B Instruct's $0.74/M output tokens - 1.3x more expensive. Input token pricing: Llama 3 70B Instruct at $0.51/M vs QwQ 32B at $0.15/M.
QwQ 32B has a larger context window of 131,072 tokens compared to Llama 3 70B Instruct's 8,192 tokens. A larger context window means the model can process longer documents and conversations.