Model Comparison
Llama 3.2 Instruct 1B
vs. 3.5 (1210)
Comparing 2 AI models · 12 benchmarks · Meta, OpenChat
Recommended Pick
Op 3.5 (1210) 10 metric wins Strongest on: Input price, Output price, TTFT
Lowest Price
3.5 (1210)
$0.00/1M input price
Best Reasoning
3.5 (1210)
13.4 reasoning score
Blends available reasoning benchmarks
Best for Coding
Llama 3.2 Instruct 1B
0.6 coding index
Composite Indices
Higher is better; speed and price are normalized
Standard Benchmarks
Only benchmarks with data are shown
Differences That Matter
Price gap
3.5 (1210) is ∞x cheaper on input tokens than Llama 3.2 Instruct 1B.
Reasoning gap
3.5 (1210) leads Llama 3.2 Instruct 1B by 6.9 points on reasoning.
Top-pick rationale
3.5 (1210) wins 10 measurable categories, including Input price, Output price, TTFT, Latency.
Response Face-Off
Run one prompt through the selected models and compare response quality with live speed and cost context.
Llama 3.2 Instruct 1B
Meta
TTFT
—
Time
—
tok/s
—
Tokens
—
Cost
—
3.5 (1210)
OpenChat
TTFT
—
Time
—
tok/s
—
Tokens
—
Cost
—
Which answer was more useful?
Full Comparison
| Metric | Me Llama 3.2 Instruct 1B | Top Pick Op 3.5 (1210) |
|---|---|---|
| Pricing per 1M tokens | ||
| Input Cost | $0.05/1M | $0.00/1M |
| Output Cost | $0.05/1M | $0.00/1M |
| Blended (3:1) | $0.05/1M | — |
| Specifications | ||
| Organization | Meta | OpenChat |
| Release Date | Sep 25, 2024 | Dec 18, 2023 |
| Performance & Speed | ||
| Throughput | 87.4 tok/s | — |
| TTFT | 595ms | — |
| Latency | 595ms | — |
| Composite Indices | ||
| Value Score | 100.0 | — |
| Reasoning Score | 6.5 | 13.4 |
| Intelligence | 6.3 | 8.3 |
| Coding | 0.6 | — |
| Math | 0.0 | — |
| Standard Benchmarks | ||
| GPQA | 19.6% | 23.0% |
| MMLU Pro | 20.0% | 31.0% |
| HLE | 5.3% | 4.8% |
| LiveCodeBench | 1.9% | 11.5% |
| MATH 500 | 14.0% | 30.7% |
| AIME 2025 | 0.0% | — |
| AIME (Original) | 0.0% | 0.0% |
| SciCode | 1.7% | — |
| LCR | 5.0% | — |
| IFBench | 22.8% | — |
| TAU-bench v2 | 0.0% | — |
| TerminalBench Hard | 0.0% | — |
Key Takeaways
3.5 (1210) offers the best value at $0.00/1M, making it ideal for high-volume applications and cost-conscious projects.
3.5 (1210) has the strongest reasoning profile with a 13.4 reasoning score, combining the available reasoning-heavy benchmarks.
Llama 3.2 Instruct 1B reaches a 0.6 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 3.2 Instruct 1B
- Code generation
- Software development
3.5 (1210)
- Cost-sensitive applications
- High-volume processing
- Complex reasoning tasks
- Research & analysis
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