Mixtral 8x22B v0.1 vs Trinity-Large-Thinking
Mixtral 8x22B v0.1 (2024) and Trinity-Large-Thinking (2026) are frontier reasoning models from MistralAI and Arcee AI. Mixtral 8x22B v0.1 ships a 64K-token context window, while Trinity-Large-Thinking ships a 256K-token context window. On Google-Proof Q&A, Trinity-Large-Thinking leads by 29.1 pts. On pricing, Trinity-Large-Thinking costs $0.22/1M input tokens versus $0.3/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Trinity-Large-Thinking fits 4x more tokens; pick it for long-context work and Mixtral 8x22B v0.1 for tighter calls.
Specs
| Released | 2024-04-17 | 2026-04-01 |
| Context window | 64K | 256K |
| Parameters | 8x22B | 400B |
| Architecture | mixture of experts | Sparse Mixture of Experts (MoE) |
| License | Apache 2.0 | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Mixtral 8x22B v0.1 | Trinity-Large-Thinking | |
|---|---|---|
| Input price | $0.3/1M tokens | $0.22/1M tokens |
| Output price | $0.9/1M tokens | $0.85/1M tokens |
| Providers |
Capabilities
| Mixtral 8x22B v0.1 | Trinity-Large-Thinking | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Mixtral 8x22B v0.1 | Trinity-Large-Thinking |
|---|---|---|
| Google-Proof Q&A | 60.1 | 89.2 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has Mixtral 8x22B v0.1 at 60.1 and Trinity-Large-Thinking at 89.2, with Trinity-Large-Thinking ahead by 29.1 points. The largest visible gap is 29.1 points on Google-Proof Q&A, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on reasoning mode: Trinity-Large-Thinking, function calling: Trinity-Large-Thinking, tool use: Trinity-Large-Thinking, and structured outputs: Trinity-Large-Thinking. 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.
For cost, Mixtral 8x22B v0.1 lists $0.3/1M input and $0.9/1M output tokens, while Trinity-Large-Thinking lists $0.22/1M input and $0.85/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Trinity-Large-Thinking lower by about $0.07 per million blended tokens. Availability is 8 providers versus 2, so concentration risk also matters.
Choose Mixtral 8x22B v0.1 when provider fit and broader provider choice are central to the workload. Choose Trinity-Large-Thinking when reasoning depth, larger context windows, and lower input-token cost are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, Mixtral 8x22B v0.1 or Trinity-Large-Thinking?
Trinity-Large-Thinking supports 256K tokens, while Mixtral 8x22B v0.1 supports 64K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, Mixtral 8x22B v0.1 or Trinity-Large-Thinking?
Trinity-Large-Thinking is cheaper on tracked token pricing. Mixtral 8x22B v0.1 costs $0.3/1M input and $0.9/1M output tokens. Trinity-Large-Thinking costs $0.22/1M input and $0.85/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Mixtral 8x22B v0.1 or Trinity-Large-Thinking open source?
Mixtral 8x22B v0.1 is listed under Apache 2.0. Trinity-Large-Thinking is listed under Apache 2.0. 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, Mixtral 8x22B v0.1 or Trinity-Large-Thinking?
Trinity-Large-Thinking 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, Mixtral 8x22B v0.1 or Trinity-Large-Thinking?
Trinity-Large-Thinking 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 Mixtral 8x22B v0.1 and Trinity-Large-Thinking?
Mixtral 8x22B v0.1 is available on NVIDIA NIM, OctoAI API, Fireworks AI, DeepInfra, and Baseten API. Trinity-Large-Thinking is available on Arcee AI and OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.