Gemini 2.5 Pro vs Grok Code Fast 1

Summary of head-to-heads in our 12-test suite (scores shown are from our tests):

• Gemini wins (8 tests): structured output 5 vs 4, strategic analysis 4 vs 3, creative problem solving 5 vs 3, tool calling 5 vs 4, faithfulness 5 vs 4, long context 5 vs 4, persona consistency 5 vs 4, multilingual 5 vs 4. These wins mean Gemini is better at JSON/schema compliance, accurate function selection and arguments, resisting hallucination, maintaining persona, and handling >30K-token retrieval. Rankings back this: Gemini is tied for 1st in long context ("tied for 1st with 36 other models out of 55 tested") and tied for 1st in structured output ("tied for 1st with 24 other models out of 54 tested") and tool calling ("tied for 1st with 16 other models out of 54 tested").
• Grok wins (2 tests): agentic planning 5 vs 4 and safety calibration 2 vs 1. Grok's agentic planning rank is "tied for 1st with 14 other models out of 54 tested," showing it is stronger at goal decomposition and failure recovery for agentic flows. Its safety calibration advantage (2 vs 1) indicates fewer false refusals or better calibration on moderation-style prompts in our suite.
• Ties (2 tests): constrained rewriting 3 vs 3 and classification 4 vs 4 (both tied in rankings). Classification is a shared strength (both tied for 1st with many models). External benchmarks: beyond our internal tests, Gemini 2.5 Pro scores 57.6% on SWE-bench Verified and 84.2% on AIME 2025 (both per Epoch AI). The payload contains no SWE-bench or AIME scores for Grok Code Fast 1. Context window and throughput tradeoffs: Gemini offers a 1,048,576-token context window vs Grok's 256,000, matching Gemini's top long context score and making it preferable for multi-document retrieval or very long chats. Conversely, Grok's smaller window plus lower price favors high-rate, lower-cost coding pipelines. Bottom line on real tasks: pick Gemini for production APIs that require strict structured output, long context, high faithfulness, multilingual support, and best-in-class tool calling. Pick Grok for cost-sensitive agentic coding, faster iteration, and cases where agentic planning is primary.

Listed prices: Gemini 2.5 Pro input $1.25/Mtok, output $10.00/Mtok; Grok Code Fast 1 input $0.20/Mtok, output $1.50/Mtok. Output-cost ratio (Gemini vs Grok) in the payload is 6.6667. Practical examples (assumes equal split of input/output tokens; we state this assumption explicitly):

• 1M total tokens (0.5M in + 0.5M out): Gemini ≈ $5.63/month, Grok ≈ $0.85/month.
• 10M total tokens (5M in + 5M out): Gemini ≈ $56.25/month, Grok ≈ $8.50/month.
• 100M total tokens (50M in + 50M out): Gemini ≈ $562.50/month, Grok ≈ $85.00/month. Who should care: teams running high-throughput production (10M–100M+ tokens/mo) will see Gemini costs multiply and should justify the premium by the quality gains (see benchmarks). Startups, prototypes, or batch-coded pipelines that prioritize cost per token should prefer Grok Code Fast 1.

Choose Gemini 2.5 Pro if you need the best long-context handling, schema-compliant outputs, tool-calling accuracy, or multilingual/faithful responses and can justify the premium price (output $10.00/Mtok). Choose Grok Code Fast 1 if you need a much lower-cost option (output $1.50/Mtok), want strong agentic planning and safety calibration in coding agents, or are optimizing for throughput and budget.