Llama 4 Behemoth vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Llama 4 Behemoth by Meta and Llama 5 by Meta 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, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, Llama 4 Behemoth provides an expanded memory horizon with 10,000K tokens. In latency-sensitive production environments, Llama 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.
Switch & Compare Any Models
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Llama 4 Behemoth
2T Open GiantMeta's gargantuan 2-Trillion parameter open model pushing the frontier of open-weight capability and 10M context.
$0.00 / 1M
$0.00 / 1M
10M tokens
75%
Llama 5
Recursive Self-ImprovementMeta's flagship open model featuring native agent training, recursive self-improvement, and 5M token context.
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Key Differences at a Glance
- Both models cost the same under this workload — choose on capability, not price.
- Llama 4 Behemoth has a larger context window (10M vs 5M tokens).
- Llama 5 is faster at 65 tok/s vs 45 tok/s.
- Llama 4 Behemoth leads on SWE-bench coding (75.0% vs 74.0%).
| Feature | Llama 4 Behemoth | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 93 | 94 |
Provider | Meta | Meta |
Input Price Cost per million input tokens | $0.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 10M tokens~15,000 A4 pages of text | 5M tokens~7,500 A4 pages of text |
Max Output Tokens Per single request | 64K | 128K |
Throughput Speed Tokens generated per second | 45 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 75% | 74% |
GPQA Diamond Graduate-level reasoning | 82% | 84% |
Who Wins Where?
At-a-glance decision frameworkEmpirical Head-to-Head Benchmark Scores
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.
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.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 4 Behemoth · Strengths & Key Advantages
- Unrivaled 2T parameter open capacity
- Staggering ~10 Million token context horizon
- State-of-the-art coding and reasoning scores
Considerations & Trade-offs
- Demands high-end datacenter multi-node GPU clusters (8x H100 minimum)
- Slow inference throughput compared to smaller models
Llama 5 · Strengths & Key Advantages
- 5 Million token context with native multi-audio/video understanding
- Recursive self-improvement training loop
- Completely open weights with enterprise commercial license
Considerations & Trade-offs
- Complex MoE serving requirements
- 4B tokenizer changes require updating legacy fine-tuning harnesses
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 4 Behemoth | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 35K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 15K | 20K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 4 Behemoth | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-4-Behemoth | meta-llama/Llama-5 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-05 | 2026-04 |
| Knowledge Cutoff | February 2026 | January 2026 |
| Architecture | Colossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE) | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) |
| Parameters | 2T total / 288B active (16-of-2 MoE) | 600B+ total (~40-80B active) |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | 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) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1240 GB VRAM (4-bit Quantized) · 4300 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 372 GB VRAM (4-bit Quantized) · 1290 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text, vision, video, audio |
| Output Modalities | text | text |
Llama 4 Behemoth vs Llama 5: Quick Answers
Which is more intelligent, Llama 4 Behemoth or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 93 on the composite Intelligence Index.
Which model is faster?
Llama 5 is faster, generating 65 tokens per second compared with 45 tokens per second.
Which model has lower latency?
Llama 5 has lower latency, with a time to first token of 0.85s compared with 1.20s.
Which model has a larger context window?
Llama 4 Behemoth has a larger context window, supporting 10M tokens compared with 5M tokens.
Verdict
Verdict & Model Selection Recommendation: Choose Llama 4 Behemoth if your priority is National laboratories, large enterprise AI clusters, and extreme scientific modeling. Its performance in demanding tasks justifies its operational token cost. Choose Llama 5 if your workflow prioritizes Autonomous open-source multi-agent systems, deep software refactoring, and frontier research. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Frequently asked questions
How does Llama 4 Behemoth compare to Llama 5 in API token pricing and cost per token?+
Both Llama 4 Behemoth and Llama 5 are open-weights foundation models available at $0.00 direct API cost when self-hosted, or offered at identical baseline rates across major inference providers. Model selection depends on GPU deployment sizing and specialized benchmark performance rather than raw token price.
Which model has a larger context window: Llama 4 Behemoth or Llama 5?+
Llama 4 Behemoth supports a context window of 10,000K tokens (~7,500k words), while Llama 5 supports 5,000K tokens (~3,750k words). Therefore, Llama 4 Behemoth can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Llama 4 Behemoth or Llama 5?+
Llama 4 Behemoth scores 75% on SWE-bench Verified while Llama 5 scores 74%. For autonomous software engineering and multi-file debugging, Llama 4 Behemoth holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Llama 4 Behemoth and Llama 5?+
Llama 4 Behemoth features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Llama 5 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Llama 4 Behemoth and Llama 5?+
Llama 4 Behemoth generates approximately 45 tokens per second, while Llama 5 delivers 65 tokens per second. In latency-sensitive production environments, Llama 5 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Llama 4 Behemoth and Llama 5 to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to Llama 5, and route deep architectural or complex reasoning queries to Llama 4 Behemoth. This balanced routing strategy maximizes system throughput and answer quality.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- Gemini 3.8 Flash vs Llama 5: Benchmarks, Pricing & Speed ComparisonCompare
- GPT-OSS 120B vs Llama 5: Benchmarks, Pricing & Speed ComparisonCompare
- Llama 4 Behemoth vs Nemotron 3 Ultra 550B A55B: Benchmarks, Pricing & Speed ComparisonCompare
- Llama 5 vs DeepSeek V4 Pro: Benchmarks, Pricing & Speed ComparisonCompare
- Llama 5 vs GPT-6 Astra: Benchmarks, Pricing & Speed ComparisonCompare
- Llama 5 vs Kimi K3: Benchmarks, Pricing & Speed ComparisonCompare
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.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs