Grok 3 Mini vs Grok 4.20
In our testing Grok 4.20 is the better pick for production workloads that need strict schema adherence, strategic reasoning and strong multilingual support. Grok 3 Mini wins on safety calibration (2 vs 1) and is far cheaper, so choose it when cost and conservative refusal behavior matter.
xai
Grok 3 Mini
Benchmark Scores
External Benchmarks
Pricing
Input
$0.300/MTok
Output
$0.500/MTok
modelpicker.net
xai
Grok 4.20
Benchmark Scores
External Benchmarks
Pricing
Input
$2.00/MTok
Output
$6.00/MTok
modelpicker.net
Benchmark Analysis
Across our 12-test suite Grok 4.20 wins 5 categories, Grok 3 Mini wins 1, and 6 are ties. Detailed per-test interpretation (scores shown are our 1–5 ratings):
- Safety calibration: Grok 3 Mini 2 vs Grok 4.20 1 — Grok 3 Mini wins here in our testing (rank 12 of 55 vs Grok 4.20 rank 32 of 55). Expect Grok 3 Mini to refuse harmful requests more reliably in our safety scenarios.
- Structured output: Grok 3 Mini 4 vs Grok 4.20 5 — Grok 4.20 wins and ranks tied for 1st of 54 on structured output while Grok 3 Mini is rank 26; choose Grok 4.20 when strict JSON/schema compliance matters.
- Strategic analysis: Grok 3 Mini 3 vs Grok 4.20 5 — Grok 4.20 wins and is tied for 1st on strategic analysis; this translates to noticeably better nuanced tradeoff reasoning in our tests.
- Creative problem solving: Grok 3 Mini 3 vs Grok 4.20 4 — Grok 4.20 performs better at generating non-obvious, feasible ideas (rank 9 of 54 for 4.20 vs rank 30 for 3 Mini).
- Agentic planning: Grok 3 Mini 3 vs Grok 4.20 4 — Grok 4.20 wins (rank 16 of 54) for goal decomposition and failure recovery in our agentic planning tests.
- Multilingual: Grok 3 Mini 4 vs Grok 4.20 5 — Grok 4.20 is tied for 1st of 55 on multilingual ability; use it when equivalent non-English quality is required.
- Long context: both 5 — tied for 1st with many models (both tied for 1st of 55); both handle 30K+ token retrieval tasks equally in our tests (note Grok 4.20's context window is 2,000,000 vs 131,072 for Grok 3 Mini in the payload).
- Tool calling: both 5 — both tied for 1st (tool selection, arguments and sequencing were top-tier for both in our tests).
- Faithfulness: both 5 — both tied for 1st, showing similarly low hallucination rates on our faithfulness tasks.
- Persona consistency: both 5 — tied for 1st (both maintain character well in our injection-resistance tests).
- Constrained rewriting: both 4 — tie (rank 6 of 53) for compression-within-limits tasks.
- Classification: both 4 — tie and tied for 1st of 53 in our classification routing tests.
Context and practical meaning: Grok 4.20 is measurably better when outputs must match a schema, when you need multi-step strategic reasoning, or when you support many languages. Grok 3 Mini is preferable if you prioritize safer refusals and much lower cost per token. Both excel at long context, tool calling and faithfulness in our testing.
Pricing Analysis
The payload lists per-mTok prices: Grok 3 Mini input $0.30 / output $0.50 per mTok; Grok 4.20 input $2 / output $6 per mTok. Assuming mTok = 1,000 tokens (so 1M tokens = 1,000 mTok), total cost per mTok is $0.80 for Grok 3 Mini and $8.00 for Grok 4.20. At 1M tokens/month that scales to $800 (3 Mini) vs $8,000 (4.20); at 10M: $8,000 vs $80,000; at 100M: $80,000 vs $800,000. The gap is ~10x. Teams with high-volume usage, tight budgets, or many small requests should care deeply about the difference; teams that need the capabilities where Grok 4.20 wins (structured output, strategic analysis, large multimodal context) may justify the higher spend.
Real-World Cost Comparison
Bottom Line
Choose Grok 3 Mini if you: need a low-cost model for high-volume use (total ≈ $0.80/mTok), prioritize safer refusal behavior, want accessible internal reasoning traces, or have budget constraints (1M tokens ≈ $800). Choose Grok 4.20 if you: require top-tier structured output (5/5), stronger strategic analysis and multilingual capability (5/5 across those tests), agentic planning, multimodal inputs, or very large context windows and can afford ~10x higher token costs (1M tokens ≈ $8,000).
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.