Grok Code Fast 1 vs Mistral Small 4

Mistral Small 4 wins the majority of benchmarks in our testing — 5 vs 2 for Grok Code Fast 1, with 5 tests tied — and costs 60% less on output tokens ($0.60 vs $1.50 per million). Grok Code Fast 1 earns its keep specifically for agentic coding workflows, where its 5/5 on agentic planning (tied for 1st of 54 models) and reasoning trace support give it a real edge. For general-purpose tasks, Mistral Small 4 delivers more breadth at a lower price.

xai

Grok Code Fast 1

Overall
3.67/5Strong

Benchmark Scores

Faithfulness
4/5
Long Context
4/5
Multilingual
4/5
Tool Calling
4/5
Classification
4/5
Agentic Planning
5/5
Structured Output
4/5
Safety Calibration
2/5
Strategic Analysis
3/5
Persona Consistency
4/5
Constrained Rewriting
3/5
Creative Problem Solving
3/5

External Benchmarks

SWE-bench Verified
N/A
MATH Level 5
N/A
AIME 2025
N/A

Pricing

Input

$0.200/MTok

Output

$1.50/MTok

Context Window256K

modelpicker.net

mistral

Mistral Small 4

Overall
3.83/5Strong

Benchmark Scores

Faithfulness
4/5
Long Context
4/5
Multilingual
5/5
Tool Calling
4/5
Classification
2/5
Agentic Planning
4/5
Structured Output
5/5
Safety Calibration
2/5
Strategic Analysis
4/5
Persona Consistency
5/5
Constrained Rewriting
3/5
Creative Problem Solving
4/5

External Benchmarks

SWE-bench Verified
N/A
MATH Level 5
N/A
AIME 2025
N/A

Pricing

Input

$0.150/MTok

Output

$0.600/MTok

Context Window262K

modelpicker.net

Benchmark Analysis

Across our 12-test suite, Mistral Small 4 outscores Grok Code Fast 1 on 5 benchmarks, Grok Code Fast 1 leads on 2, and 5 are tied.

Where Grok Code Fast 1 wins:

  • Agentic planning (5 vs 4): This is Grok Code Fast 1's standout result — tied for 1st of 54 models in our testing. It means strong goal decomposition and failure recovery, critical for autonomous coding agents that need to self-correct mid-task.
  • Classification (4 vs 2): Grok Code Fast 1 ties for 1st among 53 models here, while Mistral Small 4 ranks a weak 51st of 53. This is a meaningful gap — classification drives routing, intent detection, and triage pipelines. Mistral Small 4 is a poor choice for classification-heavy workloads.

Where Mistral Small 4 wins:

  • Structured output (5 vs 4): Mistral Small 4 ties for 1st of 54 models on JSON schema compliance — the top tier. Grok Code Fast 1 sits mid-pack at rank 26. For any application that depends on reliable structured responses, Mistral Small 4 has a clear edge.
  • Strategic analysis (4 vs 3): Mistral Small 4 ranks 27th of 54; Grok Code Fast 1 ranks 36th. A one-point gap here translates to meaningfully better nuanced tradeoff reasoning — relevant for business analysis, research summarization, and decision-support tools.
  • Creative problem solving (4 vs 3): Mistral Small 4 ranks 9th of 54, well above Grok Code Fast 1's 30th. For ideation, brainstorming, and open-ended prompts, Mistral Small 4 produces more specific and feasible ideas in our testing.
  • Persona consistency (5 vs 4): Mistral Small 4 ties for 1st of 53, while Grok Code Fast 1 sits at 38th. Chatbot and roleplay applications requiring stable character maintenance will see a real difference.
  • Multilingual (5 vs 4): Mistral Small 4 ties for 1st of 55 models; Grok Code Fast 1 ranks 36th. For non-English deployments, this gap matters significantly.

Tied benchmarks (both score equally):

  • Tool calling (4/4), faithfulness (4/4), long context (4/4), constrained rewriting (3/3), and safety calibration (2/2) are all tied. Neither model distinguishes itself on safety in our testing — both rank 12th of 55, tied with 19 others at a below-median score.

Modality note: Mistral Small 4 accepts image input (text+image->text) while Grok Code Fast 1 is text-only. This is an additional capability advantage for Mistral Small 4 on multimodal tasks.

Reasoning tokens: Grok Code Fast 1 uses reasoning tokens (visible traces in the response), which is a structural advantage for complex multi-step coding tasks even where aggregate scores are close.

BenchmarkGrok Code Fast 1Mistral Small 4
Faithfulness4/54/5
Long Context4/54/5
Multilingual4/55/5
Tool Calling4/54/5
Classification4/52/5
Agentic Planning5/54/5
Structured Output4/55/5
Safety Calibration2/52/5
Strategic Analysis3/54/5
Persona Consistency4/55/5
Constrained Rewriting3/53/5
Creative Problem Solving3/54/5
Summary2 wins5 wins

Pricing Analysis

Grok Code Fast 1 costs $0.20/MTok input and $1.50/MTok output. Mistral Small 4 costs $0.15/MTok input and $0.60/MTok output — making output tokens 2.5x cheaper on Mistral Small 4. At 1M output tokens/month, that's $1,500 vs $600 — a $900 difference. At 10M output tokens, the gap widens to $9,000/month ($15,000 vs $6,000). At 100M output tokens, you're looking at $150,000 vs $60,000 — a $90,000 annual swing. The cost gap matters most to high-volume API consumers and production applications with heavy output generation. For developers running occasional agentic coding sprints, the premium for Grok Code Fast 1's reasoning traces may be worth it. For anyone building chatbots, content pipelines, or multilingual apps, Mistral Small 4's economics are hard to ignore.

Real-World Cost Comparison

TaskGrok Code Fast 1Mistral Small 4
iChat response<$0.001<$0.001
iBlog post$0.0031$0.0013
iDocument batch$0.079$0.033
iPipeline run$0.790$0.330

Bottom Line

Choose Grok Code Fast 1 if: You are building agentic coding pipelines where step-by-step reasoning traces are valuable — its 5/5 agentic planning score (tied for 1st of 54) and visible reasoning tokens make it purpose-built for this use case. Also choose it when classification accuracy is critical (tied for 1st of 53 on classification vs Mistral Small 4's near-last rank of 51st).

Choose Mistral Small 4 if: Your workload spans general-purpose tasks — structured output generation, multilingual content, creative work, strategic analysis, or persona-driven chat. It wins 5 of 12 benchmarks in our testing, accepts image input, and costs 2.5x less per output token ($0.60 vs $1.50/MTok). At any meaningful scale, the savings compound quickly, and the broader capability profile makes it the stronger default for most API consumers.

How We Test

We test every model against our 12-benchmark suite covering tool calling, agentic planning, creative problem solving, safety calibration, and more. Each test is scored 1–5 by an LLM judge. Read our full methodology.

Frequently Asked Questions