Claude Haiku 4.5 vs Grok 4.20

Summary of our 12-test suite (scores shown are from our testing):

• Haiku wins (in our testing): safety_calibration 2 vs 1 (Haiku rank 12 of 55 vs Grok rank 32 of 55) and agentic_planning 5 vs 4 (Haiku tied for 1st, Grok rank 16 of 54). This means Haiku is more likely to calibrate refusals correctly and is stronger at goal decomposition and failure recovery on our agentic tasks.
• Grok wins (in our testing): structured_output 5 vs 4 (Grok tied for 1st on structured_output, Haiku rank 26 of 54) and constrained_rewriting 4 vs 3 (Grok rank 6 of 53, Haiku rank 31). In practice Grok is measurably better at JSON/schema compliance and squeezing content into hard character limits.
• Ties (both models match on these tests in our testing): strategic_analysis 5, creative_problem_solving 4, tool_calling 5, faithfulness 5, classification 4, long_context 5, persona_consistency 5, multilingual 5. Notably, both score 5 on tool_calling and long_context in our tests and are tied for top ranks on strategic_analysis, faithfulness, multilingual, and persona_consistency — so for general reasoning, tool workflows, multilingual output, and long-context retrieval (30K+ tokens), they perform equivalently on our suite. Context window & modalities (payload): Haiku context_window = 200,000; Grok context_window = 2,000,000. Both achieved a long_context score of 5 in our testing, but Grok exposes a much larger raw window in the metadata. Use the rank displays above when you need strict schema adherence (Grok) vs slightly stronger safety/agentic planning (Haiku).

Pricing per mTOK: Haiku 4.5 input $1 / output $5; Grok 4.20 input $2 / output $6. Example (assumes 50% input / 50% output tokens):

• 1M total tokens: Haiku = $3,000 (500 mTOK input * $1 + 500 mTOK output * $5); Grok = $4,000 (500*$2 + 500*$6). Haiku saves $1,000 per 1M tokens.
• 10M tokens: Haiku ≈ $30,000 vs Grok ≈ $40,000 (save $10,000).
• 100M tokens: Haiku ≈ $300,000 vs Grok ≈ $400,000 (save $100,000). If your workload is output-heavy (e.g., 80% output), the gap widens because output rates are higher ($5 vs $6). Teams running high-volume APIs, large-scale agents, or multi-tenant SaaS should care about this gap; individual developers or small experiments may not. All figures use the model price fields in the payload (input_cost_per_mtok, output_cost_per_mtok) and assume a straightforward input/output split — adjust calculations to your actual I/O ratio.

Choose Claude Haiku 4.5 if: you want the best price-to-performance for general purpose chat, agent workflows, and long-context tasks — it ties Grok on 8 of 12 benchmarks, wins safety_calibration and agentic_planning in our tests, and costs less (input $1/output $5). Choose Grok 4.20 if: your primary need is rigid structured output (JSON/schema) or constrained_rewriting — Grok scores 5 on structured_output and 4 on constrained_rewriting in our testing and ranks higher for those tasks. If you operate at 10M+ tokens/month, Haiku’s lower per-token rates will yield significant savings.