Llama 3.1 70B Instruct vs Qwen3 235B A22B Thinking 2507
| Signal | Llama 3.1 70B Instruct | Delta | Qwen3 235B A22B Thinking 2507 |
|---|---|---|---|
Capabilities | 50 | -17 | |
Benchmarks | 72 | +8 | |
Pricing | 100 | +3 | |
Context window size | 81 | -5 | |
Recency | 0 | -66 | |
Output Capacity | 70 | -5 | |
| Overall Result | 2 wins | of 6 | 4 wins |
Score History
65.3
current score
Qwen3 235B A22B Thinking 2507
right now
65.5
current score
Llama 3.1 70B Instruct
Meta
Qwen3 235B A22B Thinking 2507
Alibaba
Llama 3.1 70B Instruct saves you $120.00/month
That's $1440.00/year compared to Qwen3 235B A22B Thinking 2507 at your current usage level of 100K calls/month.
| Metric | Llama 3.1 70B Instruct | Qwen3 235B A22B Thinking 2507 | Winner |
|---|---|---|---|
| Overall Score | 65 | 66 | Qwen3 235B A22B Thinking 2507 |
| Rank | #142 | #141 | Qwen3 235B A22B Thinking 2507 |
| Quality Rank | #142 | #141 | Qwen3 235B A22B Thinking 2507 |
| Adoption Rank | #142 | #141 | Qwen3 235B A22B Thinking 2507 |
| Parameters | 70B | 235B | -- |
| Context Window | 131K | 262K | Qwen3 235B A22B Thinking 2507 |
| Pricing | $0.40/$0.40/M | $0.30/$3.00/M | -- |
| Signal Scores | |||
| Capabilities | 50 | 67 | Qwen3 235B A22B Thinking 2507 |
| Benchmarks | 72 | 64 | Llama 3.1 70B Instruct |
| Pricing | 100 | 97 | Llama 3.1 70B Instruct |
| Context window size | 81 | 86 | Qwen3 235B A22B Thinking 2507 |
| Recency | 0 | 66 | Qwen3 235B A22B Thinking 2507 |
| Output Capacity | 70 | 75 | Qwen3 235B A22B Thinking 2507 |
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 65/100 (rank #142), placing it in the top 51% of all 290 models tracked.
Scores 66/100 (rank #141), placing it in the top 52% 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.
Choose Llama 3.1 70B Instruct when you need:
- High-volume production workloads where API costs must be minimized
- Self-hosted deployments where you need full control over the model
Choose Qwen3 235B A22B Thinking 2507 when you need:
- Processing long documents or large codebases (262K token context)
- Step-by-step reasoning and chain-of-thought problem solving
- Self-hosted deployments where you need full control over the model
Llama 3.1 70B Instruct offers 76% better value per quality point. At 1M tokens/day, you'd spend $12.00/month with Llama 3.1 70B Instruct vs $49.50/month with Qwen3 235B A22B Thinking 2507 - a $37.50 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 3.1 70B 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 ($0.40/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (66/100) correlates with better nuance, coherence, and style in long-form content
Llama 3.1 70B Instruct and Qwen3 235B A22B Thinking 2507 are extremely close in overall performance (only 0.20000000000000284 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
By Use Case
Best for Quality
Llama 3.1 70B Instruct
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 3.1 70B Instruct
76% lower pricing; better value at scale
Best for Reliability
Llama 3.1 70B Instruct
Higher uptime and faster response speeds
Best for Prototyping
Llama 3.1 70B Instruct
Stronger community support and better developer experience
Best for Production
Llama 3.1 70B Instruct
Wider enterprise adoption and proven at scale
by Meta
- Choose for Quality - Marginally better benchmark scores; both are excellent
- Choose for Cost - 76% lower pricing; better value at scale
- Choose for Reliability - Higher uptime and faster response speeds
- Choose for Prototyping - Stronger community support and better developer experience
- Choose for Production - Wider enterprise adoption and proven at scale
| Capability | Llama 3.1 70B Instruct | Qwen3 235B A22B Thinking 2507 |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
Llama 3.1 70B Instruct
Meta
Qwen3 235B A22B Thinking 2507
Alibaba
Llama 3.1 70B Instruct saves you $2.94/month
That's 71% cheaper than Qwen3 235B A22B Thinking 2507 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 3.1 70B Instruct | Qwen3 235B A22B Thinking 2507 |
|---|---|---|
| Context Window | 131K | 262K |
| Max Output Tokens | 16,384 | 32,768 |
| Open Source | Yes | Yes |
| Created | Jul 23, 2024 | Jul 25, 2025 |