Side-by-side comparison of GPT-5.6 Sol, Mistral Large 3: pricing, capabilities, integrations and compliance — from verified HokAI records.
OpenAI
Pick it if: Sol replaces manual multi-agent orchestration for teams running the hardest coding and research workloads, not everyday chat. It's the default model inside the enterprise Copilot suite, so most business users already run it without choosing it directly, while cost-sensitive teams should route to a cheaper sibling model instead.
Its edge: State-of-the-art across coding, knowledge work, cybersecurity, and science benchmarks with 2x token efficiency vs competing flagships.
The catch: Premium pricing at $5/$30 per 1M tokens — 2.5x Terra, 5x Luna — limits high-volume use cases.
Mistral AI
Pick it if: Mistral Large 3 suits teams that want a self-hostable, openly licensed alternative to closed frontier models for coding and long-document work, scoring about 92% on HumanEval. It's the wrong pick for graduate-level science reasoning, where newer open-weight reasoning models pull well ahead, and for teams lacking the multi-GPU infrastructure needed to self-host it at scale.
Its edge: Apache 2.0 open weights at frontier scale (675B total, 41B active MoE), with no commercial-use restrictions.
The catch: GPQA Diamond score of 43.9% trails DeepSeek-V3.2 and Kimi K2-Thinking, which score 70-85% on the same benchmark.
| Input / 1M tokens | $5 | $0.5 |
|---|---|---|
| Output / 1M tokens | $30 | $1.50 |
| Cached input / 1M | $0.625 | — |
| Blended cost (3:1) | $11.25 | $0.75 |
| Free tier | false | false |
| Pricing model | per-token | per-token |
| Strengths | State-of-the-art across coding, knowledge work, cybersecurity, and science benchmarks with 2x token efficiency vs competing flagships.; Ultra multi-agent mode coordinates parallel subagents for faster complex task completion — unique at thi | Apache 2.0 open weights at frontier scale (675B total, 41B active MoE), with no commercial-use restrictions.; 256K-token context window for both input and output, double Mistral Medium 3's 128K.; Strong coding performance (~92% HumanEval pa |
|---|---|---|
| Limitations | Premium pricing at $5/$30 per 1M tokens — 2.5x Terra, 5x Luna — limits high-volume use cases.; Ultra mode and multi-agent beta features may have rough edges during early GA period.; No native audio/video I/O — vision only; multimodal laggin | GPQA Diamond score of 43.9% trails DeepSeek-V3.2 and Kimi K2-Thinking, which score 70-85% on the same benchmark.; Self-hosting requires holding all 675B parameters in VRAM (roughly 355GB at 4-bit, ~710GB at FP16) even though only 41B are ac |
| Context window | 200K | 256K |
|---|---|---|
| Max output tokens | 64K | 256K |
| Input modalities | text; image | text; image |
| Output modalities | text; tool-calls; code | text |
| Capabilities | Vision; Tool use; Code execution | Vision; Tool use; Function calling |
| Reasoning modes | none; low; medium | standard |
| Long-context recall | high | high |
| Openness | proprietary | open-source |
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