GLM-5 9B vs Llama 3.1 70B Instruct
GLM-5 9B (2026) and Llama 3.1 70B Instruct (2024) are frontier reasoning models from Zhipu AI and AI at Meta. GLM-5 9B ships a 262K-token context window, while Llama 3.1 70B Instruct ships a 128K-token context window. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
GLM-5 9B is safer overall; choose Llama 3.1 70B Instruct when provider fit matters.
Specs
| Released | 2026-02-15 | 2024-07-23 |
| Context window | 262K | 128K |
| Parameters | 9 | 70B |
| Architecture | decoder only | decoder only |
| License | Open Source | Open Source |
| Knowledge cutoff | - | - |
Pricing and availability
| GLM-5 9B | Llama 3.1 70B Instruct | |
|---|---|---|
| Input price | - | $0.4/1M tokens |
| Output price | - | $0.4/1M tokens |
| Providers | - |
Capabilities
| GLM-5 9B | Llama 3.1 70B Instruct | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on reasoning mode: GLM-5 9B, function calling: GLM-5 9B, tool use: GLM-5 9B, and structured outputs: Llama 3.1 70B Instruct. Both models share the core language-model surface, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
Pricing coverage is uneven: GLM-5 9B has no token price sourced yet and Llama 3.1 70B Instruct has $0.4/1M input tokens. Provider availability is 0 tracked routes versus 11. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose GLM-5 9B when reasoning depth and larger context windows are central to the workload. Choose Llama 3.1 70B Instruct when provider fit and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency.
FAQ
Which has a larger context window, GLM-5 9B or Llama 3.1 70B Instruct?
GLM-5 9B supports 262K tokens, while Llama 3.1 70B Instruct supports 128K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is GLM-5 9B or Llama 3.1 70B Instruct open source?
GLM-5 9B is listed under Open Source. Llama 3.1 70B Instruct is listed under Open Source. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for reasoning mode, GLM-5 9B or Llama 3.1 70B Instruct?
GLM-5 9B has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for function calling, GLM-5 9B or Llama 3.1 70B Instruct?
GLM-5 9B has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for tool use, GLM-5 9B or Llama 3.1 70B Instruct?
GLM-5 9B has the clearer documented tool use signal in this comparison. If tool use is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run GLM-5 9B and Llama 3.1 70B Instruct?
GLM-5 9B is available on the tracked providers still being sourced. Llama 3.1 70B Instruct is available on OctoAI API, Together AI, Fireworks AI, NVIDIA NIM, and Microsoft Foundry. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.