GLM-5 vs Llama 3 Taiwan 70B Instruct
GLM-5 (2026) and Llama 3 Taiwan 70B Instruct (2024) are frontier reasoning models from Zhipu AI and AI at Meta. GLM-5 ships a 200k-token context window, while Llama 3 Taiwan 70B Instruct ships a 8k-token context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads. It focuses on practical selection signals rather than broad model-family marketing.
GLM-5 fits 25x more tokens; pick it for long-context work and Llama 3 Taiwan 70B Instruct for tighter calls.
Decision scorecard
Local evidence first| Signal | GLM-5 | Llama 3 Taiwan 70B Instruct |
|---|---|---|
| Best for | reasoning-heavy apps, tool-calling agents, and provider-routed production | general production evaluation |
| Decision fit | Coding, RAG, and Agents | General |
| Context window | 200k | 8k |
| Cheapest output | $2.08/1M tokens | - |
| Provider routes | 7 tracked | 1 tracked |
| Shared benchmarks | 0 shared | 0 shared |
Decision tradeoffs
- GLM-5 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GLM-5 has broader tracked provider coverage for fallback and route flexibility.
- GLM-5 uniquely exposes Reasoning, JSON / Tool use, and Structured outputs in local model data.
- Local decision data tags GLM-5 for Coding, RAG, and Agents.
- Use Llama 3 Taiwan 70B Instruct when your own prompt tests beat the comparison signals; the local data does not show a decisive standalone advantage yet.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
GLM-5
$1,000
Cheapest tracked route/tier: OpenRouter
Llama 3 Taiwan 70B Instruct
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- Check replacement coverage for Reasoning, JSON / Tool use, and Structured outputs before moving production traffic.
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- GLM-5 adds Reasoning, JSON / Tool use, and Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-02-11 | 2024-07-01 |
| Context window | 200k | 8k |
| Parameters | 744B total, 40B active | 70B |
| Architecture | Mixture of Experts | Decoder Only |
| License | MITOSI-approved | Llama 3 Community |
| Openness | Open source | Open weights |
| Weights | Available | Unknown |
| Code | Unknown | Unknown |
| Commercial use | Commercial use: permitted | Commercial use: conditional |
| Knowledge cutoff | 2025-11 | 2023-12 |
Pricing and availability
| Pricing attribute | GLM-5 | Llama 3 Taiwan 70B Instruct |
|---|---|---|
| Input price | $0.60/1M tokens | - |
| Output price | $2.08/1M tokens | - |
| Providers |
Capabilities
| Capability | GLM-5 | Llama 3 Taiwan 70B Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | Yes | No |
| JSON / Tool use | Yes | No |
| Structured outputs | Yes | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark scores are currently available for this pair.
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Last reviewed: 2026-07-09. Data sourced from public model cards and provider documentation.