o3-pro vs Llama 4 Behemoth
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare o3-pro by OpenAI against Llama 4 Behemoth by Meta across real-world coding, reasoning, token economics, and latency.
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o3-pro
Deep-Reasoning-Modello3-pro von OpenAI ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 200K Token zu API-Preisen von $20/1M Input- und $80/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$20.00 / 1M
$80.00 / 1M
200K tokens
71.5%
Llama 4 Behemoth
Open-Weights-ModellLlama 4 Behemoth von Meta ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 10M Token zu API-Preisen von $0/1M Input- und $0/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$0.00 / 1M
$0.00 / 1M
10M tokens
75%
Wichtigste Unterschiede auf einen Blick
- Llama 4 Behemoth is 1.0x cheaper for API usage (input + output tokens combined).
- Llama 4 Behemoth has a larger context window (10M vs 200K tokens).
- Llama 4 Behemoth is faster at 45 tok/s vs 30 tok/s.
- Llama 4 Behemoth leads on SWE-bench coding (75.0% vs 71.5%).
| Feature | o3-pro | Llama 4 Behemoth |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 58 | 93 |
Provider | OpenAI | Meta |
Input Price Cost per million input tokens | $20.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $80.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $15.50 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 10M tokens~15,000 A4 pages of text |
Max Output Tokens Per single request | 100K | 64K |
Throughput Speed Tokens generated per second | 30 tok/s | 45 tok/s |
SWE-bench Verified Real-world software engineering | 71.5% | 75% |
GPQA Diamond Graduate-level reasoning | 79.5% | 82% |
Cached Input Price Prompt caching discount per 1M tokens | $5.00 / 1M | $0.00 / 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.
o3-pro · Strengths & Key Advantages
- Fortschrittliche High-effort reasoning transformer-Architektur von OpenAI mit 200K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($20/1M Input, $80/1M 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
Llama 4 Behemoth · Strengths & Key Advantages
- Fortschrittliche Colossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE)-Architektur von Meta mit 10M Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($0/1M Input, $0/1M 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 | o3-pro | Llama 4 Behemoth |
|---|---|---|
Cost per Task Estimated USD to finish one task | $11.68 | $0.00 |
Output Tokens / Task Tokens generated per task | 58K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 38K | 15K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | o3-pro | Llama 4 Behemoth |
|---|---|---|
| Official API Model ID (SDK) | o3-pro | meta-llama/Llama-4-Behemoth |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-06 | 2026-05 |
| Knowledge Cutoff | June 2024 | February 2026 |
| Architecture | High-effort reasoning transformer | Colossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE) |
| Parameters | Confidential | 2T total / 288B active (16-of-2 MoE) |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($20/1M in) across full 200K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $5/1M (no extra cache-write fee) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| 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) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1240 GB VRAM (4-bit Quantized) · 4300 GB VRAM (Native BF16) |
| Safety & Frontier Classification | OpenAI Preparedness Framework Audited & System Card Published | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
o3-pro vs Llama 4 Behemoth: Schnelle Antworten (FAQ)
Which is more intelligent, o3-pro or Llama 4 Behemoth?
Llama 4 Behemoth is more intelligent. It scores 93 compared with 58 on the composite Intelligence Index.
Which model is faster?
Llama 4 Behemoth is faster, generating 45 tokens per second compared with 30 tokens per second.
Which model is cheaper to run?
Llama 4 Behemoth is cheaper at $0.00 per 1M tokens blended, compared with $15.50 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 4 Behemoth has lower latency, with a time to first token of 1.20s compared with 5.20s.
Which model has a larger context window?
Llama 4 Behemoth has a larger context window, supporting 10M tokens compared with 200K tokens.
Fazit
Choose o3-pro for unternehmensautomatisierung, software-engineering, autonome ki-agenten und openai-produktionssysteme, or choose Llama 4 Behemoth for unternehmensautomatisierung, software-engineering, autonome ki-agenten und meta-produktionssysteme.
Häufig gestellte Fragen (FAQ)
Which is cheaper: o3-pro or Llama 4 Behemoth?+
o3-pro costs $20/1M input and $80/1M output tokens, while Llama 4 Behemoth costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: o3-pro or Llama 4 Behemoth?+
o3-pro supports 200K tokens whereas Llama 4 Behemoth supports 10M tokens.
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: OpenAI & Meta Official Documentation