| Signal | Llama 3 8B Instruct | Delta | GLM 4 32B |
|---|---|---|---|
Capabilities | 33 | -- | |
Benchmarks | 32 | -12 | |
Pricing | 100 | +0 | |
Context window size | 56 | -17 | |
Recency | 0 | -77 | |
Output Capacity | 65 | +45 | |
| Overall Result | 2 wins | of 6 | 3 wins |
Score History
34.2
current score
GLM 4 32B
right now
35.2
current score
Meta
Zhipu AI
Llama 3 8B Instruct saves you $9.00/month
That's $108.00/year compared to GLM 4 32B at your current usage level of 100K calls/month.
| Metric | Llama 3 8B Instruct | GLM 4 32B | Winner |
|---|---|---|---|
| Overall Score | 34 | 35 | GLM 4 32B |
| Rank | #320 | #319 | GLM 4 32B |
| Quality Rank | #320 | #319 | GLM 4 32B |
| Adoption Rank | #320 | #319 | GLM 4 32B |
| Parameters | 8B | 32B | -- |
| Context Window | 8K | 128K | GLM 4 32B |
| Pricing | $0.04/$0.04/M | $0.10/$0.10/M | -- |
| Signal Scores | |||
| Capabilities | 33 | 33 | Llama 3 8B Instruct |
| Benchmarks | 32 | 44 | GLM 4 32B |
| Pricing | 100 | 100 | Llama 3 8B Instruct |
| Context window size | 56 | 73 | GLM 4 32B |
| Recency | 0 | 77 | GLM 4 32B |
| Output Capacity | 65 | 20 | Llama 3 8B Instruct |
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 34/100 (rank #320), placing it in the top -10% of all 290 models tracked.
Scores 35/100 (rank #319), placing it in the top -10% of all 290 models tracked.
With only a 1-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.
Llama 3 8B Instruct offers 60% better value per quality point. At 1M tokens/day, you'd spend $1.20/month with Llama 3 8B Instruct vs $3.00/month with GLM 4 32B - a $1.80 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 8B Instruct also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (128K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.04/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (35/100) correlates with better nuance, coherence, and style in long-form content
Llama 3 8B Instruct and GLM 4 32B are extremely close in overall performance (only 1 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Llama 3 8B Instruct
Marginally better benchmark scores; both are excellent
Best for Cost
Llama 3 8B Instruct
60% lower pricing; better value at scale
Best for Reliability
Llama 3 8B Instruct
Higher uptime and faster response speeds
Best for Prototyping
Llama 3 8B Instruct
Stronger community support and better developer experience
Best for Production
Llama 3 8B Instruct
Wider enterprise adoption and proven at scale
by Meta
| Capability | Llama 3 8B Instruct | GLM 4 32B |
|---|---|---|
| Vision (Image Input) | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
Meta
Zhipu AI
Llama 3 8B Instruct saves you $0.1800/month
That's 60% cheaper than GLM 4 32B 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 8B Instruct | GLM 4 32B |
|---|---|---|
| Context Window | 8K | 128K |
| Max Output Tokens | 8,192 | -- |
| Open Source | Yes | No |
| Created | Apr 18, 2024 | Jul 24, 2025 |