Llama 3 Taiwan 70B Instruct vs Mistral Small 4
Llama 3 Taiwan 70B Instruct (2024) and Mistral Small 4 (2026) are compact production models from AI at Meta and MistralAI. Llama 3 Taiwan 70B Instruct ships a 8k-token context window, while Mistral Small 4 ships a 256k-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.
Mistral Small 4 fits 32x more tokens; pick it for long-context work and Llama 3 Taiwan 70B Instruct for tighter calls.
Decision scorecard
Local evidence first| Signal | Llama 3 Taiwan 70B Instruct | Mistral Small 4 |
|---|---|---|
| Best for | general production evaluation | multimodal apps, tool-calling agents, and provider-routed production |
| Decision fit | General | RAG, Agents, and Long context |
| Context window | 8k | 256k |
| Cheapest output | - | $0.30/1M tokens |
| Provider routes | 1 tracked | 3 tracked |
| Shared benchmarks | 0 shared | 0 shared |
Decision tradeoffs
- 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.
- Mistral Small 4 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Mistral Small 4 has broader tracked provider coverage for fallback and route flexibility.
- Mistral Small 4 uniquely exposes Vision, Multimodal, and JSON / Tool use in local model data.
- Local decision data tags Mistral Small 4 for RAG, Agents, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
Llama 3 Taiwan 70B Instruct
Unavailable
No complete token price in local provider data
Mistral Small 4
$155
Cheapest tracked route/tier: Mistral AI Studio
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.
- Mistral Small 4 adds Vision, Multimodal, and JSON / Tool use in local capability data.
- Provider overlap exists on NVIDIA NIM; start route-level A/B tests there.
- Check replacement coverage for Vision, Multimodal, and JSON / Tool use before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-07-01 | 2026-03-16 |
| Context window | 8k | 256k |
| Parameters | 70B | 119B (6.5B active) |
| Architecture | Decoder Only | Mixture of Experts |
| License | Llama 3 Community | Apache 2.0OSI-approved |
| Openness | Open weights | Open source |
| Weights | Unknown | Available |
| Code | Unknown | Unknown |
| Commercial use | Commercial use: conditional | Commercial use: permitted |
| Knowledge cutoff | 2023-12 | 2025-06 |
Pricing and availability
| Pricing attribute | Llama 3 Taiwan 70B Instruct | Mistral Small 4 |
|---|---|---|
| Input price | - | $0.10/1M tokens |
| Output price | - | $0.30/1M tokens |
| Providers |
Capabilities
| Capability | Llama 3 Taiwan 70B Instruct | Mistral Small 4 |
|---|---|---|
| Vision | No | Yes |
| Multimodal | No | Yes |
| Reasoning | No | No |
| JSON / Tool use | No | Yes |
| Structured outputs | No | 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.