Model Comparison
Llama 3.1 Instruct 70B
vs. Qwen3.5 27B (Reasoning)
Comparing 2 AI models · 12 benchmarks · Meta, Alibaba
Recommended Pick
Al Qwen3.5 27B (Reasoning) 13 metric wins Strongest on: Value, Input price, Throughput
Best Value
Qwen3.5 27B (Reasoning)
100.0 value score
50.0 reasoning / $0.82/1M
Lowest Price
Qwen3.5 27B (Reasoning)
$0.30/1M input price
Best Reasoning
Qwen3.5 27B (Reasoning)
50.0 reasoning score
Blends available reasoning benchmarks
Best for Coding
Qwen3.5 27B (Reasoning)
34.9 coding index
Composite Indices
Higher is better; speed and price are normalized
Standard Benchmarks
Only benchmarks with data are shown
Differences That Matter
Best value
Qwen3.5 27B (Reasoning) has the strongest quality-to-price mix at 100.0 out of 100 value points.
Price gap
Qwen3.5 27B (Reasoning) is 1.9x cheaper on input tokens than Llama 3.1 Instruct 70B.
Speed gap
Qwen3.5 27B (Reasoning) generates about 2.1x as many tokens per second as Llama 3.1 Instruct 70B.
Reasoning gap
Qwen3.5 27B (Reasoning) leads Llama 3.1 Instruct 70B by 28.9 points on reasoning.
Coding gap
Qwen3.5 27B (Reasoning) leads Llama 3.1 Instruct 70B by 24.0 points on coding.
Response Face-Off
Run one prompt through the selected models and compare response quality with live speed and cost context.
Llama 3.1 Instruct 70B
Meta
TTFT
—
Time
—
tok/s
—
Tokens
—
Cost
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Qwen3.5 27B (Reasoning)
Alibaba
TTFT
—
Time
—
tok/s
—
Tokens
—
Cost
—
Which answer was more useful?
Full Comparison
| Metric | Me Llama 3.1 Instruct 70B | Top Pick Al Qwen3.5 27B (Reasoning) |
|---|---|---|
| Pricing per 1M tokens | ||
| Input Cost | $0.56/1M | $0.30/1M |
| Output Cost | $0.56/1M | $2.40/1M |
| Blended (3:1) | $0.56/1M | $0.82/1M |
| Specifications | ||
| Organization | Meta | Alibaba |
| Release Date | Jul 23, 2024 | Feb 24, 2026 |
| Performance & Speed | ||
| Throughput | 39.9 tok/s | 83.4 tok/s |
| TTFT | 531ms | 1387ms |
| Latency | 531ms | 25357ms |
| Composite Indices | ||
| Value Score | 62.4 | 100.0 |
| Reasoning Score | 21.2 | 50.0 |
| Intelligence | 12.5 | 42.1 |
| Coding | 10.9 | 34.9 |
| Math | 4.0 | — |
| Standard Benchmarks | ||
| GPQA | 40.9% | 85.8% |
| MMLU Pro | 67.6% | — |
| HLE | 4.6% | 22.2% |
| LiveCodeBench | 23.2% | — |
| MATH 500 | 64.9% | — |
| AIME 2025 | 4.0% | — |
| AIME (Original) | 17.3% | — |
| SciCode | 26.7% | 39.5% |
| LCR | 6.3% | 67.3% |
| IFBench | 34.4% | 75.6% |
| TAU-bench v2 | 15.2% | 93.9% |
| TerminalBench Hard | 3.0% | 32.6% |
Key Takeaways
Qwen3.5 27B (Reasoning) offers the best value at $0.30/1M, making it ideal for high-volume applications and cost-conscious projects.
Qwen3.5 27B (Reasoning) has the strongest reasoning profile with a 50.0 reasoning score, combining the available reasoning-heavy benchmarks.
Qwen3.5 27B (Reasoning) reaches a 34.9 coding index, making it the top choice for software development and code generation tasks.
All models support context windows of ∞+ tokens, suitable for processing lengthy documents and maintaining extended conversations.
When to Choose Each Model
Llama 3.1 Instruct 70B
- General-purpose AI
- Versatile applications
Qwen3.5 27B (Reasoning)
- Cost-sensitive applications
- High-volume processing
- Complex reasoning tasks
- Research & analysis
- Code generation
- Software development
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