Claude Haiku 4.5 vs Gemini 2.5 Flash Lite
In our testing Claude Haiku 4.5 is the better choice for complex reasoning, planning, and classification (it wins 5 of 12 tests). Gemini 2.5 Flash Lite is the practical choice when cost and throughput matter — it wins constrained rewriting and costs far less ($0.50 per mTok total vs $6 per mTok for Haiku).
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Claude Haiku 4.5
Benchmark Scores
External Benchmarks
Pricing
Input
$1.00/MTok
Output
$5.00/MTok
modelpicker.net
Gemini 2.5 Flash Lite
Benchmark Scores
External Benchmarks
Pricing
Input
$0.100/MTok
Output
$0.400/MTok
modelpicker.net
Benchmark Analysis
Summary of our 12-test suite (scores are 1–5 in our testing):
- Claude Haiku 4.5 wins (in our testing) on strategic_analysis (5 vs 3), creative_problem_solving (4 vs 3), classification (4 vs 3), safety_calibration (2 vs 1), and agentic_planning (5 vs 4). Notably Haiku’s strategic_analysis score of 5 is tied for 1st ("tied for 1st with 25 other models out of 54 tested"), and agentic_planning 5 is also tied for 1st, which indicates strong performance for nuanced tradeoffs and multi‑step goal decomposition in real tasks.
- Gemini 2.5 Flash Lite wins constrained_rewriting (4 vs 3). On that test Gemini ranks 6 of 53 ("rank 6 of 53 (25 models share this score)"), showing it’s measurably better for tight compression or hard character‑limit rewrites in our suite.
- Ties: structured_output (4/4), tool_calling (5/5), faithfulness (5/5), long_context (5/5), persona_consistency (5/5), multilingual (5/5). Both models score 5 on long_context and faithfulness and are tied for 1st in those categories, so for retrieval across 30k+ tokens and sticking to source material you’ll get similar behavior in our tests.
- Safety: Haiku scored 2 vs Flash Lite 1; Haiku’s safety_calibration ranks 12 of 55 while Flash Lite ranks 32 of 55 — in practice Haiku is more likely to correctly refuse harmful requests and permit legitimate ones in our tests, though neither is perfect. Taken together: Haiku’s high scores and top ranks on strategic_analysis, agentic_planning, and classification translate into better performance for decision support, multi‑step agents, and routing tasks. Flash Lite’s single clear win (constrained_rewriting) and much lower cost make it strong for high‑volume, cost‑sensitive rewriting/compression workflows and throughput‑focused deployments. Both models tie on tool calling and long‑context, so function selection and large‑context retrieval were equivalent in our evaluation.
Pricing Analysis
Raw per‑mTok pricing from the payload: Claude Haiku 4.5 charges $1 input + $5 output = $6.00 per 1k tokens; Gemini 2.5 Flash Lite charges $0.10 input + $0.40 output = $0.50 per 1k tokens. Monthly cost examples (input+output combined):
- 1M tokens/month (1,000 mTok): Haiku = $6,000; Flash Lite = $500.
- 10M tokens/month (10,000 mTok): Haiku = $60,000; Flash Lite = $5,000.
- 100M tokens/month (100,000 mTok): Haiku = $600,000; Flash Lite = $50,000. Who should care: teams doing high‑volume production inference (chat fleets, search, analytics at tens of millions+ tokens) will see large savings with Gemini 2.5 Flash Lite. Projects that prioritize the highest reasoning/agentic capability per request (fewer requests but higher per‑call quality) may justify Haiku’s ~12x higher per‑token cost.
Real-World Cost Comparison
Bottom Line
Choose Claude Haiku 4.5 if you need best-in-class reasoning, multi‑step agent planning, accurate classification, or safer refusals in fewer, higher‑value API calls (Haiku wins 5 of 12 tests and ranks tied for 1st on several reasoning categories). Choose Gemini 2.5 Flash Lite if you need an ultra‑cost‑efficient, low‑latency model for high-volume workloads or constrained rewriting — it wins constrained_rewriting and costs $0.50 per mTok vs Haiku’s $6 per mTok. If you must balance both, use Flash Lite for bulk, low-cost inference and Haiku for premium decision or synthesis calls.
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.