Model Comparison
GLM-4.6 (Non-reasoning)
vs. Llama 3.1 Instruct 8B
Comparing 2 AI models · 12 benchmarks · Z AI, Meta
Recommended Pick
Z GLM-4.6 (Non-reasoning) 14 metric wins Strongest on: Reasoning, Intelligence, Coding
Best Value
Llama 3.1 Instruct 8B
100.0 value score
15.9 reasoning / $0.10/1M
Lowest Price
Llama 3.1 Instruct 8B
$0.10/1M input price
Best Reasoning
GLM-4.6 (Non-reasoning)
37.4 reasoning score
Blends available reasoning benchmarks
Best for Coding
GLM-4.6 (Non-reasoning)
30.2 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
Llama 3.1 Instruct 8B has the strongest quality-to-price mix at 100.0 out of 100 value points.
Price gap
Llama 3.1 Instruct 8B is 6.0x cheaper on input tokens than GLM-4.6 (Non-reasoning).
Speed gap
Llama 3.1 Instruct 8B generates about 3.1x as many tokens per second as GLM-4.6 (Non-reasoning).
Reasoning gap
GLM-4.6 (Non-reasoning) leads Llama 3.1 Instruct 8B by 21.6 points on reasoning.
Coding gap
GLM-4.6 (Non-reasoning) leads Llama 3.1 Instruct 8B by 25.3 points on coding.
Response Face-Off
Run one prompt through the selected models and compare response quality with live speed and cost context.
GLM-4.6 (Non-reasoning)
Z AI
TTFT
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Time
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tok/s
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Tokens
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Cost
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Llama 3.1 Instruct 8B
Meta
TTFT
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Time
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tok/s
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Tokens
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Cost
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Which answer was more useful?
Full Comparison
| Metric | Top Pick Z GLM-4.6 (Non-reasoning) | Me Llama 3.1 Instruct 8B |
|---|---|---|
| Pricing per 1M tokens | ||
| Input Cost | $0.60/1M | $0.10/1M |
| Output Cost | $2.20/1M | $0.10/1M |
| Blended (3:1) | $1.00/1M | $0.10/1M |
| Specifications | ||
| Organization | Z AI | Meta |
| Release Date | Sep 30, 2025 | Jul 23, 2024 |
| Performance & Speed | ||
| Throughput | 61.7 tok/s | 193.6 tok/s |
| TTFT | 1798ms | 495ms |
| Latency | 1798ms | 495ms |
| Composite Indices | ||
| Value Score | 23.6 | 100.0 |
| Reasoning Score | 37.4 | 15.9 |
| Intelligence | 30.2 | 11.8 |
| Coding | 30.2 | 4.9 |
| Math | 44.3 | 4.3 |
| Standard Benchmarks | ||
| GPQA | 63.2% | 25.9% |
| MMLU Pro | 78.4% | 47.6% |
| HLE | 5.2% | 5.1% |
| LiveCodeBench | 56.1% | 11.6% |
| MATH 500 | — | 51.9% |
| AIME 2025 | 44.3% | 4.3% |
| AIME (Original) | — | 7.7% |
| SciCode | 33.1% | 13.2% |
| LCR | 26.3% | 15.7% |
| IFBench | 36.7% | 28.6% |
| TAU-bench v2 | 76.9% | 16.4% |
| TerminalBench Hard | 28.8% | 0.8% |
Key Takeaways
Llama 3.1 Instruct 8B offers the best value at $0.10/1M, making it ideal for high-volume applications and cost-conscious projects.
GLM-4.6 (Non-reasoning) has the strongest reasoning profile with a 37.4 reasoning score, combining the available reasoning-heavy benchmarks.
GLM-4.6 (Non-reasoning) reaches a 30.2 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
GLM-4.6 (Non-reasoning)
- Complex reasoning tasks
- Research & analysis
- Code generation
- Software development
Llama 3.1 Instruct 8B
- Cost-sensitive applications
- High-volume processing
