Model Comparison
GLM-4.7 (Non-reasoning)
vs. Llama 4 Scout
Comparing 2 AI models · 12 benchmarks · Z AI, Meta
Recommended Pick
Z GLM-4.7 (Non-reasoning) 14 metric wins Strongest on: Reasoning, Intelligence, Coding
Best Value
Llama 4 Scout
100.0 value score
31.0 reasoning / $0.29/1M
Lowest Price
Llama 4 Scout
$0.17/1M input price
Best Reasoning
GLM-4.7 (Non-reasoning)
40.5 reasoning score
Blends available reasoning benchmarks
Best for Coding
GLM-4.7 (Non-reasoning)
32.0 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 4 Scout has the strongest quality-to-price mix at 100.0 out of 100 value points.
Price gap
Llama 4 Scout is 3.5x cheaper on input tokens than GLM-4.7 (Non-reasoning).
Speed gap
Llama 4 Scout generates about 1.2x as many tokens per second as GLM-4.7 (Non-reasoning).
Reasoning gap
GLM-4.7 (Non-reasoning) leads Llama 4 Scout by 9.5 points on reasoning.
Coding gap
GLM-4.7 (Non-reasoning) leads Llama 4 Scout 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-4.7 (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 4 Scout
Meta
TTFT
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Time
—
tok/s
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Tokens
—
Cost
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Which answer was more useful?
Full Comparison
| Metric | Top Pick Z GLM-4.7 (Non-reasoning) | Me Llama 4 Scout |
|---|---|---|
| Pricing per 1M tokens | ||
| Input Cost | $0.60/1M | $0.17/1M |
| Output Cost | $2.20/1M | $0.66/1M |
| Blended (3:1) | $1.00/1M | $0.29/1M |
| Specifications | ||
| Organization | Z AI | Meta |
| Release Date | Dec 22, 2025 | Apr 5, 2025 |
| Performance & Speed | ||
| Throughput | 83.0 tok/s | 99.1 tok/s |
| TTFT | 853ms | 642ms |
| Latency | 853ms | 642ms |
| Composite Indices | ||
| Value Score | 38.2 | 100.0 |
| Reasoning Score | 40.5 | 31.0 |
| Intelligence | 34.2 | 13.5 |
| Coding | 32.0 | 6.7 |
| Math | 48.0 | 14.0 |
| Standard Benchmarks | ||
| GPQA | 66.4% | 58.7% |
| MMLU Pro | 79.4% | 75.2% |
| HLE | 6.1% | 4.3% |
| LiveCodeBench | 56.2% | 29.9% |
| MATH 500 | — | 84.4% |
| AIME 2025 | 48.0% | 14.0% |
| AIME (Original) | — | 28.3% |
| SciCode | 35.4% | 17.0% |
| LCR | 36.3% | 25.8% |
| IFBench | 54.6% | 39.5% |
| TAU-bench v2 | 94.2% | 15.5% |
| TerminalBench Hard | 30.3% | 1.5% |
Key Takeaways
Llama 4 Scout offers the best value at $0.17/1M, making it ideal for high-volume applications and cost-conscious projects.
GLM-4.7 (Non-reasoning) has the strongest reasoning profile with a 40.5 reasoning score, combining the available reasoning-heavy benchmarks.
GLM-4.7 (Non-reasoning) reaches a 32.0 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-4.7 (Non-reasoning)
- Complex reasoning tasks
- Research & analysis
- Code generation
- Software development
Llama 4 Scout
- Cost-sensitive applications
- High-volume processing
