Claude 3 Haiku vs Gemini 1.5 Flash
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Claude 3 Haiku by Anthropic and Gemini 1.5 Flash by Google represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Gemini 1.5 Flash holds a notable pricing advantage for token ingestion. On output generation, Gemini 1.5 Flash is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Gemini 1.5 Flash provides an expanded memory horizon with 1,049K tokens. In latency-sensitive production environments, Gemini 1.5 Flash leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
Changez et Comparez les Modèles
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Claude 3 Haiku
Ultra-rapide et légerd'Anthropic ultra-rapide, compact modèle conçu pour instant responsiveness, léger document auditing, et coût-conscious production pipelines.
$0.25 / 1M
$1.25 / 1M
200K tokens
10.5%
Gemini 1.5 Flash
Contexte 1M pour quelques centimesélevé-vitesse, coût-optimized bourreau de travail fournissant un 1M token fenêtre de contexte à ultra-faible tarification, conçu pour élevé-frequency faible-latence production applications.
$0.07 / 1M
$0.30 / 1M
1.05M tokens
20%
Différences Clés en un Coup d’Œil
- Gemini 1.5 Flash is 3.7x cheaper for API usage (input + output tokens combined).
- Gemini 1.5 Flash has a larger context window (1M vs 200K tokens).
- Gemini 1.5 Flash is faster at 180 tok/s vs 125 tok/s.
- Gemini 1.5 Flash leads on SWE-bench coding (20.0% vs 10.5%).
| Caractéristique | Claude 3 Haiku | Gemini 1.5 Flash |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 33 | 38 |
Provider | Anthropic | |
Input Price Cost per million input tokens | $0.25 / 1M | $0.07 / 1M |
Output Price Cost per million output tokens | $1.25 / 1M | $0.30 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.19 / 1M | $0.06 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 1.05M tokens~1,573 A4 pages of text |
Max Output Tokens Per single request | 4K | 8K |
Throughput Speed Tokens generated per second | 125 tok/s | 180 tok/s |
SWE-bench Verified Real-world software engineering | 10.5% | 20% |
GPQA Diamond Graduate-level reasoning | 33% | 42.1% |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 1M | $0.02 / 1M |
Qui gagne où ?
At-a-glance decision frameworkScores de Benchmarks Vérifiés
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Claude 3 Haiku · Strengths & Key Advantages
- Processes 20,000 words de recherche dans sous 3 seconds
- Extremely coût-effective ($0.25/$1.25 par 1M tokens) avec prompt caching discounts
- complet 200,000 token fenêtre de contexte sur un léger modèle
Considerations & Trade-offs
- codage et mathématique raisonnement scores inférieur que phare tiers
- pas suited pour multifichier architectural refactorisation
Gemini 1.5 Flash · Strengths & Key Advantages
- Phenomenal prix efficacité ($0.075 par 1M entrée tokens)
- Blazing rapide débit (180 tokens/sec) avec complet 1M contexte support
- Comprehensive multimodal support including vidéo et audio ingestion
Considerations & Trade-offs
- codage et mathématique raisonnement scores inférieur que Gemini 1.5 Pro ou Gemini 2.0
- Occasional hallucinations sur deeply nuanced logique queries
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Claude 3 Haiku | Gemini 1.5 Flash |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $0.20 |
Output Tokens / Task Tokens generated per task | 24K | 24K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Claude 3 Haiku | Gemini 1.5 Flash |
|---|---|---|
| Official API Model ID (SDK) | claude-3-haiku-20240301 | models/gemini-1-5-flash |
| API Protocol | Anthropic Messages API + AWS Bedrock + Vertex AI | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2024-03 | 2024-05 |
| Knowledge Cutoff | August 2023 | May 2024 |
| Architecture | Compact High-Throughput Transformer | Distilled Sparse MoE Multimodal |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | $0.025/1M cached read (90% off); 5-min default & 1-hour extended TTL | Implicit + Explicit Context Caching at $0.01875/1M with configurable TTL |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Native Web Search Citations + Code Execution Tool + MCP Connectors | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | Custom Model Tuning via Amazon Bedrock & Prompt Distillation | Vertex AI Supervised Adapter Tuning & Context Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated | Google DeepMind Frontier Safety Framework Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text, vision, audio, video |
| Output Modalities | text | text |
Claude 3 Haiku vs Gemini 1.5 Flash: Réponses Rapides (FAQ)
Which is more intelligent, Claude 3 Haiku or Gemini 1.5 Flash?
Gemini 1.5 Flash is more intelligent. It scores 38 compared with 33 on the composite Intelligence Index.
Which model is faster?
Gemini 1.5 Flash is faster, generating 180 tokens per second compared with 125 tokens per second.
Which model is cheaper to run?
Gemini 1.5 Flash is cheaper at $0.06 per 1M tokens blended, compared with $0.19 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini 1.5 Flash has lower latency, with a time to first token of 0.32s compared with 0.35s.
Which model has a larger context window?
Gemini 1.5 Flash has a larger context window, supporting 1.05M tokens compared with 200K tokens.
Verdict
Verdict & Model Selection Recommendation: Choose Claude 3 Haiku if your priority is Real-time customer chat, instant content moderation, and high-frequency document triage. Its performance in demanding tasks justifies its operational token cost. Choose Gemini 1.5 Flash if your workflow prioritizes High-volume production APIs, customer support bots, document batch processing, and live transcription. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Questions fréquentes
How does Claude 3 Haiku compare to Gemini 1.5 Flash in API token pricing and cost per token?+
Claude 3 Haiku costs $0.25 per 1M input tokens and $1.25 per 1M output tokens, whereas Gemini 1.5 Flash costs $0.07 input and $0.30 output. Overall, Gemini 1.5 Flash is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Claude 3 Haiku or Gemini 1.5 Flash?+
Claude 3 Haiku supports a context window of 200K tokens (~150k words), while Gemini 1.5 Flash supports 1,049K tokens (~786k words). Therefore, Gemini 1.5 Flash can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Claude 3 Haiku or Gemini 1.5 Flash?+
Claude 3 Haiku scores 10.5% on SWE-bench Verified while Gemini 1.5 Flash scores 20%. For autonomous software engineering and multi-file debugging, Gemini 1.5 Flash holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Claude 3 Haiku and Gemini 1.5 Flash?+
Claude 3 Haiku is optimized for low-latency direct generation. In contrast, Gemini 1.5 Flash focuses on standard fast inference.
How does inference speed (tokens per second) compare between Claude 3 Haiku and Gemini 1.5 Flash?+
Claude 3 Haiku generates approximately 125 tokens per second, while Gemini 1.5 Flash delivers 180 tokens per second. In latency-sensitive production environments, Gemini 1.5 Flash provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Claude 3 Haiku and Gemini 1.5 Flash to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Gemini 1.5 Flash to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Claude 3 Haiku. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs