Claude Opus 4.6 vs Gemini 3.1 Flash Lite Preview
For professional, long-context and agentic workflows (coding, agents), choose Claude Opus 4.6 — it wins more benchmarks in our 12-test suite and scores 5/5 on tool calling, long context, and agentic planning. Choose Gemini 3.1 Flash Lite Preview when cost and volume matter: it’s materially cheaper (output $1.50 vs $25.00 per mTok) and wins on structured output and constrained rewriting.
anthropic
Claude Opus 4.6
Benchmark Scores
External Benchmarks
Pricing
Input
$5.00/MTok
Output
$25.00/MTok
modelpicker.net
Gemini 3.1 Flash Lite Preview
Benchmark Scores
External Benchmarks
Pricing
Input
$0.250/MTok
Output
$1.50/MTok
modelpicker.net
Benchmark Analysis
Summary from our 12-test suite (scores are our testing unless otherwise noted):
- Wins for Claude Opus 4.6 (our testing): creative_problem_solving 5 vs 4 (Claude ranks tied 1st of 54 on creative_problem_solving), tool_calling 5 vs 4 (Claude tied for 1st of 54; Gemini rank 18/54), long_context 5 vs 4 (Claude tied for 1st of 55; Gemini rank 38/55) and agentic_planning 5 vs 4 (Claude tied for 1st of 54; Gemini rank 16/54). These results mean Claude is stronger for multi-step agent workflows, accurate function selection/arguments, retrieval over 30K+ tokens, and non-obvious idea generation.
- Wins for Gemini 3.1 Flash Lite Preview (our testing): structured_output 5 vs 4 (Gemini tied for 1st of 54; Claude rank 26/54) and constrained_rewriting 4 vs 3 (Gemini rank 6/53; Claude rank 31/53). This shows Gemini is better at strict JSON/schema compliance and compression within hard character limits.
- Ties (our testing): strategic_analysis (both 5, tied for 1st), faithfulness (both 5, tied for 1st), classification (both 3), safety_calibration (both 5, tied for 1st), persona_consistency (both 5, tied for 1st), and multilingual (both 5, tied for 1st). In practice those ties indicate comparable performance for nuanced tradeoff reasoning, staying faithful to sources, safety refusals/approvals, persona adherence, and multilingual output.
- External benchmarks (Epoch AI): Claude Opus 4.6 scores 78.7% on SWE-bench Verified (Epoch AI), ranking 1 of 12 on that coding benchmark in the payload; it also scores 94.4 on AIME 2025 (rank 4 of 23) in our data. These external results supplement our internal proxies and help explain Claude’s advantage on coding and math-intense problems. Gemini has no SWE-bench or AIME external scores in the payload. Practical meaning: pick Claude when you need best-in-class agent behavior, long-context document work, and top coding/math capability (supported by SWE-bench 78.7% for Claude in Epoch AI). Pick Gemini when you need accurate schema output, compact rewrites, and much lower per-token cost.
Pricing Analysis
Pricing (per mTok = per 1k tokens in the payload): Claude Opus 4.6 charges $25.00 per 1k output tokens (input $5.00/1k); Gemini 3.1 Flash Lite Preview charges $1.50 per 1k output tokens (input $0.25/1k). At 1M output tokens/month (1,000 mTok): Claude = $25,000; Gemini = $1,500. At 10M: Claude = $250,000; Gemini = $15,000. At 100M: Claude = $2,500,000; Gemini = $150,000. The price ratio in the payload is ~16.67x (Claude vs Gemini) on output tokens. Who should care: high-volume products, chatbacks, or analytics pipelines that send millions of tokens/month will see dramatic savings with Gemini; teams building expensive agentic workflows, multi-step code generation, or one-off high-value professional tasks may justify Claude’s higher cost for the quality and feature set it delivers.
Real-World Cost Comparison
Bottom Line
Choose Claude Opus 4.6 if you need: professional agentic workflows, multi-step code generation, retrieval-heavy tasks over 30K+ tokens, or top creative problem-solving — Claude wins 4 of 12 tests including tool_calling and long_context and holds strong external coding scores (SWE-bench Verified 78.7% in Epoch AI). Choose Gemini 3.1 Flash Lite Preview if you need: high-volume, cost-sensitive production (output $1.50/1k vs Claude $25.00/1k), strict JSON/schema compliance, or constrained rewriting — Gemini wins structured_output and constrained_rewriting while costing ~16.7x less per output token.
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.