o4-mini vs Claude Haiku 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
o4-mini by OpenAI and Claude Haiku 5.5 by Anthropic 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, Claude Haiku 5.5 holds a notable pricing advantage for token ingestion. On output generation, o4-mini is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 200K tokens. In latency-sensitive production environments, Claude Haiku 5.5 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.
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o4-mini
Deep-Reasoning-Modello4-mini von OpenAI ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 200K Token zu API-Preisen von $1.1/1M Input- und $4.4/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$1.10 / 1M
$4.40 / 1M
200K tokens
57.5%
Claude Haiku 5.5
Deep-Reasoning-ModellClaude Haiku 5.5 von Anthropic ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 200K Token zu API-Preisen von $1/1M Input- und $5/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$1.00 / 1M
$5.00 / 1M
200K tokens
68.4%
Wichtigste Unterschiede auf einen Blick
- o4-mini is 1.0x cheaper for API usage (input + output tokens combined).
- Claude Haiku 5.5 is faster at 160 tok/s vs 105 tok/s.
- Claude Haiku 5.5 leads on SWE-bench coding (68.4% vs 57.5%).
| Feature | o4-mini | Claude Haiku 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 50 | 62 |
Provider | OpenAI | Anthropic |
Input Price Cost per million input tokens | $1.10 / 1M | $1.00 / 1M |
Output Price Cost per million output tokens | $4.40 / 1M | $5.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.85 / 1M | $0.77 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 200K tokens~300 A4 pages of text |
Max Output Tokens Per single request | 100K | 64K |
Throughput Speed Tokens generated per second | 105 tok/s | 160 tok/s |
SWE-bench Verified Real-world software engineering | 57.5% | 68.4% |
GPQA Diamond Graduate-level reasoning | 73% | 69.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.28 / 1M | $0.10 / 1M |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkGeprüfte Benchmark-Ergebnisse im Vergleich
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.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latenz & Antwortzeit
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Interaktiver Token-Kosten-Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
o4-mini · Strengths & Key Advantages
- Fortschrittliche Compact reasoning transformer-Architektur von OpenAI mit 200K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($1.1/1M Input, $4.4/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Claude Haiku 5.5 · Strengths & Key Advantages
- Fortschrittliche Ultra-Fast Lightweight Reasoning Transformer-Architektur von Anthropic mit 200K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($1/1M Input, $5/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | o4-mini | Claude Haiku 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.72 | $0.70 |
Output Tokens / Task Tokens generated per task | 45K | 20K |
Reasoning Tokens / Task Thinking tokens burned per task | 25K | 10K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | o4-mini | Claude Haiku 5.5 |
|---|---|---|
| Official API Model ID (SDK) | o4-mini | claude-haiku-5-5-20261001 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2025-04 | 2026-10 |
| Knowledge Cutoff | June 2024 | August 2026 |
| Architecture | Compact reasoning transformer | Ultra-Fast Lightweight Reasoning Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($1.1/1M in) across full 200K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.275/1M (no extra cache-write fee) | $0.1/1M cached read (90% off); 5-min default & 1-hour extended 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 tool + Hosted Code Interpreter & File Search RAG | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Custom Model Tuning via Amazon Bedrock & Prompt 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) |
| 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 | OpenAI Preparedness Framework Audited & System Card Published | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated |
| 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 |
| Output Modalities | text | text |
o4-mini vs Claude Haiku 5.5: Schnelle Antworten (FAQ)
Which is more intelligent, o4-mini or Claude Haiku 5.5?
Claude Haiku 5.5 is more intelligent. It scores 62 compared with 50 on the composite Intelligence Index.
Which model is faster?
Claude Haiku 5.5 is faster, generating 160 tokens per second compared with 105 tokens per second.
Which model is cheaper to run?
Claude Haiku 5.5 is cheaper at $0.77 per 1M tokens blended, compared with $0.85 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Claude Haiku 5.5 has lower latency, with a time to first token of 0.22s compared with 1.80s.
Fazit
Verdict & Model Selection Recommendation: Choose o4-mini if your priority is Budget reasoning inside agent pipelines and coding assistants. Its performance in demanding tasks justifies its operational token cost. Choose Claude Haiku 5.5 if your workflow prioritizes Real-time user-facing chatbots, rapid text classification, code completion, and edge agent routing. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Häufig gestellte Fragen (FAQ)
How does o4-mini compare to Claude Haiku 5.5 in API token pricing and cost per token?+
o4-mini costs $1.10 per 1M input tokens and $4.40 per 1M output tokens, whereas Claude Haiku 5.5 costs $1.00 input and $5.00 output. Overall, Claude Haiku 5.5 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: o4-mini or Claude Haiku 5.5?+
Both o4-mini and Claude Haiku 5.5 offer an identical context window of 200K tokens (~150k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: o4-mini or Claude Haiku 5.5?+
o4-mini scores 57.5% on SWE-bench Verified while Claude Haiku 5.5 scores 68.4%. For autonomous software engineering and multi-file debugging, Claude Haiku 5.5 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between o4-mini and Claude Haiku 5.5?+
o4-mini features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Claude Haiku 5.5 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between o4-mini and Claude Haiku 5.5?+
o4-mini generates approximately 105 tokens per second, while Claude Haiku 5.5 delivers 160 tokens per second. In latency-sensitive production environments, Claude Haiku 5.5 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining o4-mini and Claude Haiku 5.5 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Claude Haiku 5.5 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to o4-mini. 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.
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
Offizielle Quellen: GetInfoUs Independent AI Benchmarks & Official API Specs