Grok Code Fast 1 vs Ministral 3 8B 2512

Neither model has been through our full 12-test benchmark suite yet — bench_avg_score is null for both — but we do have individual test scores across all 12 dimensions that paint a clear picture.

Grok Code Fast 1's standout result is agentic planning: 5/5, tied for 1st of 54 models in our testing. This reflects the model's design as a reasoning-first coding tool — it can decompose goals, sequence steps, and recover from failure in ways that a 3/5 score (rank 42 of 54) like Ministral's simply cannot match in practice. For any workflow involving autonomous agents, tool chains, or multi-step code generation, this gap is consequential.

Safety calibration also favors Grok Code Fast 1: 2/5 (rank 12 of 55) versus Ministral's 1/5 (rank 32 of 55). Both scores are below the field median of 2, so neither model is strong here in absolute terms, but Grok Code Fast 1 is the better-calibrated of the two — it more reliably refuses harmful requests while permitting legitimate ones.

Ministral 3 8B 2512 wins decisively on constrained rewriting: 5/5, tied for 1st with only 4 other models out of 53 tested. This is a genuinely elite score — compressing text within hard character limits is a specific, measurable skill, and Ministral excels at it. For copywriting, SEO snippets, UI text, or any task requiring tight length control, this matters.

Persona consistency also goes to Ministral: 5/5 (tied for 1st of 53) versus Grok Code Fast 1's 4/5 (rank 38 of 53). Ministral maintains character and resists prompt injection more reliably in our tests, which is valuable for chatbot and roleplay applications.

The remaining eight benchmarks are ties: classification (4/5 each, tied for 1st of 53 with 30 models), tool calling (4/5 each, rank 18 of 54), structured output (4/5 each, rank 26 of 54), long context (4/5 each, rank 38 of 55), faithfulness (4/5 each, rank 34 of 55), multilingual (4/5 each, rank 36 of 55), strategic analysis (3/5 each, rank 36 of 54), and creative problem solving (3/5 each, rank 30 of 54). On strategic analysis and creative problem solving, both models sit below the field median of 4/5 — neither is a strong choice for open-ended reasoning or novel ideation tasks.

Neither model has external benchmark scores (SWE-bench Verified, AIME 2025, MATH Level 5) in the payload, so we can't supplement with third-party coding or math data.

Ministral 3 8B 2512 is dramatically cheaper: $0.15/MTok for both input and output, versus Grok Code Fast 1's $0.20/MTok input and $1.50/MTok output. At typical output-heavy usage, the difference compounds fast. At 1M output tokens/month, Grok Code Fast 1 costs $1.50 versus Ministral's $0.15 — a $1.35 gap that feels trivial. At 10M output tokens, that's $15.00 vs $1.50, a $13.50 difference worth noticing. At 100M output tokens — realistic for production document processing, summarization, or chat applications — Grok Code Fast 1 costs $150.00 versus Ministral's $15.00, a $135 monthly delta that demands justification. Developers running agentic pipelines where reasoning traces generate significant token output should model this carefully: Grok Code Fast 1's reasoning tokens add to output cost. If you're not extracting value from that reasoning, you're paying a 10x premium for nothing. Ministral 3 8B 2512 also accepts image input (text+image->text modality), which could replace a separate vision model and further reduce stack costs.

Choose Grok Code Fast 1 if your priority is agentic coding — autonomous agents, multi-step pipelines, or any workflow where goal decomposition and failure recovery matter. Its 5/5 agentic planning score (tied for 1st of 54 in our testing) and visible reasoning traces are purpose-built for this use case, and the $0.20/$1.50 pricing is defensible when the task genuinely requires it. Also prefer Grok Code Fast 1 if safety calibration is a concern — its 2/5 score outperforms Ministral's 1/5 in our tests.

Choose Ministral 3 8B 2512 if output volume is high and the task doesn't require autonomous planning. At $0.15/MTok for both input and output — 10x cheaper on output — it's the rational choice for document processing, summarization, content generation, and chat. Its 5/5 constrained rewriting score (tied for 1st of 53, only 5 models share this) makes it an especially strong fit for copy editing, character-limited writing, and structured content tasks. Its multimodal capability (text+image->text) is an added bonus if your stack includes image understanding. Developers who need tight persona control — chatbots, branded assistants — will also appreciate its 5/5 persona consistency score.