Llama 3.1 8B vs GLM 5.1
At a Glance
| Compare |  Llama 3.1 8BMeta |  GLM 5.1Z.ai |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $1.05Deepinfra ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $3.50Deepinfra ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 131K | 203K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 28, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
Side-by-Side Facts
| Field | Llama-3.1-8B | GLM-5.1 |
|---|---|---|
| Developer | Meta | Z.ai |
| Family | Llama 3 1 8b | Glm 5 1 |
| Model | Llama-3.1-8B | GLM-5.1 |
| Version | Llama-3.1-8B | GLM-5.1 |
| Lifecycle | active | active |
| Released | 2024-07-23 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 131K | 203K |
| Total parameters | 8B | 753.9B |
| Active parameters | Unknown | Unknown |
| License | llama3.1 | mit |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Hugging Face (Standard) | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | generation | chat, generation, reasoning, tools |
Llama 3.1 8B Capabilities
GLM 5.1 Capabilities
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 DeepSeek-V4.1-FlashDeepSeek | vs | Llama-3.1-8BMeta | cross-developer peerstext |
Llama-3.1-8BMeta | vs | Grok 4.6xAI | cross-developer peerstext |
Llama-3.1-8BMeta | vs |  Qwen3.8-MaxQwen | cross-developer peerstext |
Llama-3.1-8BMeta | vs |  Kimi-K3Moonshot AI | cross-developer peerstext |
 MiniMax-M3MiniMax | vs | Llama-3.1-8BMeta | cross-developer peerstext |
Llama-3.1-8BMeta | vs | GLM-5.3Z.ai | cross-developer peerstext |
Llama-3.1-8BMeta | vs |  Hy4 previewTencent | cross-developer peerstext |
Seed 2.1 ProByteDance Seed | vs | Llama-3.1-8BMeta | cross-developer peerstext |
Llama-3.1-8BMeta | vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Llama 3.1 8B vs GLM 5.1 FAQs
Is Llama 3.1 8B or GLM 5.1 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 3.1 8B and GLM 5.1, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Llama 3.1 8B or GLM 5.1?+
Only GLM 5.1 has a directly sourced input price: $1.05 per million tokens. Only GLM 5.1 has a directly sourced output price: $3.50 per million tokens.
Which has a larger context window, Llama 3.1 8B or GLM 5.1?+
GLM 5.1 has the larger sourced context window. Llama 3.1 8B supports 131K and GLM 5.1 supports 203K.
Which performs better in benchmarks, Llama 3.1 8B or GLM 5.1?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Llama 3.1 8B or GLM 5.1 be self-hosted?+
Both models have the same recorded self-hosting status: supported. Llama 3.1 8B is open weight; GLM 5.1 is open weight.
Can Llama 3.1 8B and GLM 5.1 understand images?+
Llama 3.1 8B is not documented with image input; GLM 5.1 is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Llama 3.1 8B or GLM 5.1?+
Neither has a larger sourced maximum output. Llama 3.1 8B is — and GLM 5.1 is —.
Do Llama 3.1 8B and GLM 5.1 support reasoning and tool use?+
Llama 3.1 8B: none of these features are definitively sourced. GLM 5.1: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Llama 3.1 8B or GLM 5.1?+
Llama 3.1 8B has 1 sourced provider route; GLM 5.1 has 4, so GLM 5.1 has broader tracked availability.
Which offers better value, Llama 3.1 8B or GLM 5.1?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.