GLM-5.1 vs Llama 3.1 70B Instruct
GLM-5.1 (2026) and Llama 3.1 70B Instruct (2024) are frontier reasoning models from Zhipu AI and AI at Meta. GLM-5.1 ships a 200k-token context window, while Llama 3.1 70B Instruct ships a 128k-token context window. On pricing, Llama 3.1 70B Instruct costs $0.40/1M input tokens versus $0.98/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
Llama 3.1 70B Instruct is ~145% cheaper at $0.40/1M; pay for GLM-5.1 only for coding workflow support.
Decision scorecard
Local evidence first| Signal | GLM-5.1 | Llama 3.1 70B Instruct |
|---|---|---|
| Best for | reasoning-heavy apps, tool-calling agents, and provider-routed production | provider-routed production |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Long context |
| Context window | 200k | 128k |
| Cheapest output | $3.08/1M tokens | $0.40/1M tokens |
| Provider routes | 5 tracked | 13 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- GLM-5.1 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GLM-5.1 uniquely exposes Reasoning, Function calling, and Tool use in local model data.
- Local decision data tags GLM-5.1 for Coding, RAG, and Agents.
- Llama 3.1 70B Instruct has the lower cheapest tracked output price at $0.40/1M tokens.
- Llama 3.1 70B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Llama 3.1 70B Instruct for Coding, RAG, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
GLM-5.1
$1,554
Cheapest tracked route/tier: Z.ai
Llama 3.1 70B Instruct
$420
Cheapest tracked route/tier: Hyperbolic AI Inference
Estimated monthly gap: $1,134. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI, OpenRouter, and Vercel AI Gateway; start route-level A/B tests there.
- Llama 3.1 70B Instruct is $2.68/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Reasoning, Function calling, and Tool use before moving production traffic.
- Provider overlap exists on OpenRouter, Fireworks AI, and Vercel AI Gateway; start route-level A/B tests there.
- GLM-5.1 is $2.68/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- GLM-5.1 adds Reasoning, Function calling, and Tool use in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-04-07 | 2024-07-23 |
| Context window | 200k | 128k |
| Parameters | 754B total, 40B active | 70B |
| Architecture | mixture of experts | decoder only |
| License | MIT(OSI) | Llama 3 Community |
| Openness | Open source | Open weights |
| Commercial use | Commercial use allowed | Commercial use with conditions |
| Knowledge cutoff | 2025-11 | 2023-12 |
Pricing and availability
| Pricing attribute | GLM-5.1 | Llama 3.1 70B Instruct |
|---|---|---|
| Input price | $0.98/1M tokens | $0.40/1M tokens |
| Output price | $3.08/1M tokens | $0.40/1M tokens |
| Providers |
Capabilities
| Capability | GLM-5.1 | Llama 3.1 70B Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | Yes | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | Yes |
| Code execution | Yes | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on reasoning mode: GLM-5.1, function calling: GLM-5.1, tool use: GLM-5.1, and code execution: GLM-5.1. Both models share structured outputs, 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.
For cost, GLM-5.1 lists $0.98/1M input and $3.08/1M output tokens on the cheapest tracked provider, while Llama 3.1 70B Instruct lists $0.40/1M input and $0.40/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 3.1 70B Instruct lower by about $1.21 per million blended tokens. Availability is 5 providers versus 13, so concentration risk also matters.
Choose GLM-5.1 when coding workflow support and larger context windows are central to the workload. Choose Llama 3.1 70B Instruct when provider fit, lower input-token cost, 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-5.1 or Llama 3.1 70B Instruct?
GLM-5.1 supports 200k 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.
Which is cheaper, GLM-5.1 or Llama 3.1 70B Instruct?
Llama 3.1 70B Instruct is cheaper on tracked token pricing. GLM-5.1 costs $0.98/1M input and $3.08/1M output tokens. Llama 3.1 70B Instruct costs $0.40/1M input and $0.40/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GLM-5.1 or Llama 3.1 70B Instruct open source?
GLM-5.1 is listed under MIT. Llama 3.1 70B Instruct is listed under Llama 3 Community. 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-5.1 or Llama 3.1 70B Instruct?
GLM-5.1 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-5.1 or Llama 3.1 70B Instruct?
GLM-5.1 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.
Where can I run GLM-5.1 and Llama 3.1 70B Instruct?
GLM-5.1 is available on Z.ai, OpenRouter, Fireworks AI, Vercel AI Gateway, and Novita AI. Llama 3.1 70B Instruct is available on Cloudflare Workers AI, OctoAI API (Deprecated), Together AI, Fireworks AI, and NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-05-25. Data sourced from public model cards and provider documentation.