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Model comparison
Mistral AI's Mistral Large 4 against xAI's Grok 4.5: vendor list prices, limits, abilities and the Artificial Analysis indexes side by side - and what OpenSmartRoute does when both are available.
Mistral Large 4 lists at $2.06 per million tokens blended against $3.00 for Grok 4.5 - 1.5x less.
Intelligence index 38 against 39: within a point of each other.
Mistral Large 4 reads 1.0M tokens at once, Grok 4.5 500k.
Mistral Large 4 was released about 3 months later.
Vendor list prices per million tokens and published limits; indexes are Artificial Analysis composites (0-100).
| Specification | Mistral Large 4 | Grok 4.5 |
|---|---|---|
| Input price / 1M tokens | $1.36 | $2.00 |
| Output price / 1M tokens | $4.18 | $6.00 |
| Blended (3 in : 1 out) | $2.06 | $3.00 |
| Cached input / 1M | $0.14 | $0.30 |
| Context window | 1.0M | 500K |
| Max output | 262K | 450K |
| Input | text, image | text, image, file |
| Output | text | text |
| Tool calls | ||
| Structured output | ||
| Reasoning | ||
| Open weights | ||
| Intelligence index | 38 | 39 |
| Coding index | - | 72 |
| Agentic index | - | 41 |
| Released | Oct 6, 2026 | Jul 8, 2026 |
| Available on this deployment |
List price times tokens for three typical workloads; your mix of input and output decides which model's pricing suits you.
| Workload | Mistral Large 4 | Grok 4.5 | Difference |
|---|---|---|---|
| Support chat20M tokens in, 5M out a month - a help desk answering a few thousand conversations. | $48.10 | $70.00 | $21.90 less on Mistral Large 4 |
| Document summaries100M in, 5M out - long inputs, short answers. | $157 | $230 | $73.10 less on Mistral Large 4 |
| Coding assistant60M in, 20M out - a team's editor assistant, output-heavy. | $165 | $240 | $74.80 less on Mistral Large 4 |
The two measure the same on the intelligence index, so the router treats them as peers and lets price, observed latency and your outcomes decide: Mistral Large 4 wins on cost, and a few bad outcomes on either move the next requests to the other.
# targets.yaml - both models as routing targets (metadata.model links each to the catalogue entry)
targets:
- { id: mistral-large-4-0, kind: llm, metadata: { model: mistralai/mistral-large-4-0 } }
- { id: grok-4.5, kind: llm, metadata: { model: x-ai/grok-4.5 } }
# rules.yaml - the cheaper model by default, the stronger one when the request is hard
rules:
- name: simple-requests-stay-cheap
when: { max_complexity: 0.45 }
prefer: [mistral-large-4-0]
weight: 0.8
- name: hard-requests-escalate
when: { min_complexity: 0.45 }
prefer: [grok-4.5]
weight: 0.8Every Mistral AI and xAI model, with prices, is on the vendor pages; the rankings show what the router actually picks.