Mistral Large 3 2512 vs Grok 4.3
Mistral AIxAIboth via one key, provider price + 3%
Mistral Large 3 2512 is a sparse mixture-of-experts model (41B active, 675B total params) released under Apache 2.0 in December 2025. Grok 4.3 is xAI's reasoning model, released roughly five months later with a 1M-token context window and configurable reasoning effort. Both run on OpenKey with one API key and the same flat 3% fee on top of provider list price — the difference here is entirely in what you get for that price.
Spec vs spec
| Spec | Mistral Large 3 2512 | Grok 4.3 |
|---|---|---|
| Context window | 262K | 1M |
| Input modalities | text, image, file | text, image, file |
| Output modalities | text | text |
| Released | Dec 1, 2025 | Apr 30, 2026 |
| Reasoning | — | optional |
Pricing
Per 1M tokens. Provider price plus the flat 3% fee — the sum is what you pay.
mistralai/mistral-large-2512
Input · 1M tokens
$0.500 + 3%$0.515
Output · 1M tokens
$1.50 + 3%$1.54
Cache read · 1M tokens
$0.050 + 3%$0.052
FEE — FLAT, EVERY MODEL3%
x-ai/grok-4.3
Input · 1M tokens
$1.25 + 3%$1.29
Output · 1M tokens
$2.50 + 3%$2.58
Cache read · 1M tokens
$0.200 + 3%$0.206
FEE — FLAT, EVERY MODEL3%
One workload, priced on both
10M input + 2M output tokens at each model's price, flat 3% fee included.
mistralai/mistral-large-2512Cheaper
$8.24
$8.00 provider + 3%
x-ai/grok-4.3
$18.03
$17.50 provider + 3%
Benchmarks
Design Arena categories where both models have results. Higher Elo and lower rank win.
| Mistral Large 3 2512 | Grok 4.3 | |||
|---|---|---|---|---|
| Category | Elo | Rank | Elo | Rank |
| 3D | 1176 | #51 | 1202 | #43 |
| ASCII art | 1115 | #43 | 1189 | #27 |
| Code | 1191 | #56 | 1243 | #31 |
| Data viz | 1180 | #55 | 1234 | #35 |
| Game dev | 1146 | #65 | 1242 | #34 |
| SVG | 1050 | #62 | 1140 | #44 |
| UI components | 1157 | #59 | 1250 | #31 |
| Websites | 1205 | #53 | 1244 | #31 |
Head-to-head preference voting. How we filter and rank
Pricing math on a real workload
OpenKey prices are provider price × 1.03. Mistral Large 3 2512 is $0.50/$1.50 per million tokens (input/output) at the provider, so $0.515/$1.545 on OpenKey. Grok 4.3 is $1.25/$2.50 provider, $1.2875/$2.575 on OpenKey — that includes a $0.20/M cache-read rate that Mistral doesn't disclose beyond its own $0.05/M cache read.
Run the same job on both: 10M input tokens + 2M output tokens. Mistral Large 3 2512 costs $8.00 total. Grok 4.3 costs $17.50 — 2.2x more. The input price ratio is 0.4, meaning Grok's input tokens alone cost 2.5x what Mistral's do. If you're processing high volume with simple tasks, that gap compounds fast.
Coding and agentic performance
This is where the price gap gets justified. Grok 4.3 scores 42.2 on the artificial_analysis coding index versus Mistral's 20.1 — more than double. On agentic tasks the gap is even wider: 24.1 vs 5.5. Grok 4.3 also has dedicated agent benchmarks from Design Arena across categories like webapps (elo 1194, rank 14), mobileapps (elo 1146, rank 26), and fullstack (elo 1072, rank 26) — Mistral has no agent-arena data at all, only model-arena scores.
On Design Arena's model-arena categories, Grok 4.3 also leads across the board: codecategories (1243 vs 1191), uicomponent (1250 vs 1157), and website (1244 vs 1205). If you're building anything agentic — multi-step coding, tool-calling workflows — Grok 4.3's numbers back up the higher price.
Context and long-document work
Grok 4.3 offers 1,000,000 tokens of context. Mistral Large 3 2512 offers 262,144 — a context ratio of 0.26, meaning Mistral holds roughly a quarter of what Grok can. If you're feeding in full codebases, long transcripts, or multi-document research bundles, Grok 4.3's window gives you room Mistral simply doesn't have. For anything under ~200K tokens per request, the difference won't matter in practice.
Reasoning and control
Grok 4.3 supports configurable reasoning effort — high, medium, low, or none — with low as the default and reasoning enabled by default. It also exposes `logprobs` and `top_logprobs` as supported parameters, useful if you need token-level probability inspection. Mistral Large 3 2512 has no reasoning field at all — it's a standard completion model with `tool_choice`, `tools`, and `structured_outputs` support but no effort dial. If your workload benefits from letting the model think longer on hard steps, Grok 4.3 gives you that lever; Mistral doesn't.
Which model for which job
| Use case | Pick | Why |
|---|---|---|
| High-volume simple extraction/classification | Mistral Large 3 2512 | $8.00 vs $17.50 on a 10M-in/2M-out workload — less than half the cost |
| Agentic coding / multi-step tool use | Grok 4.3 | Agentic index of 24.1 vs 5.5, with dedicated agent-arena benchmarks Mistral doesn't have |
| Long-document or full-codebase analysis | Grok 4.3 | 1M token context vs Mistral's 262K — nearly 4x the room |
| Open-weight or license-sensitive deployment | Mistral Large 3 2512 | Released under Apache 2.0, unlike Grok 4.3 |
| Tasks needing adjustable reasoning depth | Grok 4.3 | Supports high/medium/low/none reasoning effort; Mistral has no reasoning parameter |
| UI/frontend component generation | Grok 4.3 | uicomponent elo 1250 (rank 31) vs Mistral's 1157 (rank 59) |
Questions
- Which is cheaper, Mistral Large 3 2512 or Grok 4.3?
- Mistral Large 3 2512, by a wide margin. On OpenKey it's $0.515/$1.545 per million input/output tokens versus Grok 4.3's $1.2875/$2.575. On a 10M-input/2M-output workload, that's $8.00 for Mistral versus $17.50 for Grok — Grok costs 2.2x more.
- Which model has more context?
- Grok 4.3, with a 1,000,000-token context window versus Mistral Large 3 2512's 262,144 tokens. That's a context ratio of 0.26 — Mistral can hold about a quarter of what Grok can in a single request.
- Is Grok 4.3 better at coding?
- Yes. Grok 4.3 scores 42.2 on the artificial_analysis coding index against Mistral's 20.1, and its agentic index is 24.1 versus 5.5. Design Arena also shows Grok ahead in every model-arena category, including codecategories (1243 vs 1191 elo).
- Does either model support adjustable reasoning?
- Grok 4.3 does — it supports reasoning efforts of high, medium, low, or none, with reasoning enabled by default at low effort. Mistral Large 3 2512 has no reasoning parameter in its supported parameter list.