Mistral Large 3 675B Instruct vs Trinity-Large-Thinking
Mistral Large 3 675B Instruct (2025) and Trinity-Large-Thinking (2026) are frontier reasoning models from MistralAI and Arcee AI. Mistral Large 3 675B Instruct ships a 128k-token context window, while Trinity-Large-Thinking ships a 256k-token context window. On Google-Proof Q&A, Trinity-Large-Thinking leads by 45.3 pts. On pricing, Trinity-Large-Thinking costs $0.22/1M input tokens versus $0.50/1M 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.
Trinity-Large-Thinking is ~127% cheaper at $0.22/1M; pay for Mistral Large 3 675B Instruct only for vision-heavy evaluation.
Decision scorecard
Local evidence first| Signal | Mistral Large 3 675B Instruct | Trinity-Large-Thinking |
|---|---|---|
| Best for | multimodal apps and provider-routed production | reasoning-heavy apps, tool-calling agents, and provider-routed production |
| Decision fit | Coding, RAG, and Agents | RAG, Agents, and Long context |
| Context window | 128k | 256k |
| Cheapest output | $1.50/1M tokens | $0.85/1M tokens |
| Provider routes | 5 tracked | 3 tracked |
| Shared benchmarks | 1 rows | Google-Proof Q&A leader |
Decision tradeoffs
- Mistral Large 3 675B Instruct has broader tracked provider coverage for fallback and procurement flexibility.
- Mistral Large 3 675B Instruct uniquely exposes Vision and Multimodal in local model data.
- Local decision data tags Mistral Large 3 675B Instruct for Coding, RAG, and Agents.
- Trinity-Large-Thinking holds a shared-benchmark lead on Google-Proof Q&A, ahead by 45.3 points.
- Trinity-Large-Thinking has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Trinity-Large-Thinking has the lower cheapest tracked output price at $0.85/1M tokens.
- Trinity-Large-Thinking uniquely exposes Reasoning, Function calling, and Tool use in local model data.
- Local decision data tags Trinity-Large-Thinking 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.
Mistral Large 3 675B Instruct
$775
Cheapest tracked route/tier: AWS Bedrock
Trinity-Large-Thinking
$389
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $387. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Vercel AI Gateway; start route-level A/B tests there.
- Trinity-Large-Thinking is $0.65/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Vision and Multimodal before moving production traffic.
- Trinity-Large-Thinking adds Reasoning, Function calling, and Tool use in local capability data.
- Provider overlap exists on Vercel AI Gateway; start route-level A/B tests there.
- Mistral Large 3 675B Instruct is $0.65/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Reasoning, Function calling, and Tool use before moving production traffic.
- Mistral Large 3 675B Instruct adds Vision and Multimodal in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-12-01 | 2026-04-01 |
| Context window | 128k | 256k |
| Parameters | 675B | 400B |
| Architecture | decoder only | Sparse Mixture of Experts (MoE) |
| License | Mistral License | Apache 2.0(OSI) |
| Openness | Open weights | Open source |
| Commercial use | Non-commercial only | Commercial use allowed |
| Knowledge cutoff | 2024-11 | - |
Pricing and availability
| Pricing attribute | Mistral Large 3 675B Instruct | Trinity-Large-Thinking |
|---|---|---|
| Input price | $0.50/1M tokens | $0.22/1M tokens |
| Output price | $1.50/1M tokens | $0.85/1M tokens |
| Providers |
Capabilities
| Capability | Mistral Large 3 675B Instruct | Trinity-Large-Thinking |
|---|---|---|
| Vision | Yes | No |
| Multimodal | Yes | No |
| Reasoning | No | Yes |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Mistral Large 3 675B Instruct | Trinity-Large-Thinking |
|---|---|---|
| Google-Proof Q&A | 43.9 | 89.2 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has Mistral Large 3 675B Instruct at 43.9 and Trinity-Large-Thinking at 89.2, with Trinity-Large-Thinking ahead by 45.3 points. The largest visible gap is 45.3 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 vision: Mistral Large 3 675B Instruct, multimodal input: Mistral Large 3 675B Instruct, reasoning mode: Trinity-Large-Thinking, function calling: Trinity-Large-Thinking, and tool use: Trinity-Large-Thinking. 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, Mistral Large 3 675B Instruct lists $0.50/1M input and $1.50/1M output tokens on the cheapest tracked provider, 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 $0.39 per million blended tokens. Availability is 5 providers versus 3, so concentration risk also matters.
Choose Mistral Large 3 675B Instruct when vision-heavy evaluation 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, Mistral Large 3 675B Instruct or Trinity-Large-Thinking?
Trinity-Large-Thinking supports 256k tokens, while Mistral Large 3 675B 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, Mistral Large 3 675B Instruct or Trinity-Large-Thinking?
Trinity-Large-Thinking is cheaper on tracked token pricing. Mistral Large 3 675B Instruct costs $0.50/1M input and $1.50/1M 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 Mistral Large 3 675B Instruct or Trinity-Large-Thinking open source?
Mistral Large 3 675B Instruct is listed under Mistral License. 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 vision, Mistral Large 3 675B Instruct or Trinity-Large-Thinking?
Mistral Large 3 675B Instruct has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for multimodal input, Mistral Large 3 675B Instruct or Trinity-Large-Thinking?
Mistral Large 3 675B Instruct has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Mistral Large 3 675B Instruct and Trinity-Large-Thinking?
Mistral Large 3 675B Instruct is available on AWS Bedrock, NVIDIA NIM, Mistral AI Studio, Microsoft Foundry, and Vercel AI Gateway. Trinity-Large-Thinking is available on Arcee AI, OpenRouter, and Vercel AI Gateway. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-06-04. Data sourced from public model cards and provider documentation.