Llama 5 vs Kimi K3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Llama 5 by Meta and Kimi K3 by Moonshot AI 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 5 provides an expanded memory horizon with 5,000K 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.
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Llama 5
オープンウェイトモデルMeta が提供する Llama 5 は、最大 5M トークンのコンテキストウィンドウを備え、入力 $0/100万トークン・出力 $0/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Kimi K3
オープンウェイトモデルMoonshot AI が提供する Kimi K3 は、最大 1.0M トークンのコンテキストウィンドウを備え、入力 $0/100万トークン・出力 $0/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.00 / 1M
$0.00 / 1M
1.05M tokens
74.5%
ひと目でわかる主な違い
- Both models cost the same under this workload — choose on capability, not price.
- Llama 5 has a larger context window (5M vs 1M tokens).
- Llama 5 is faster at 65 tok/s vs 58 tok/s.
- Kimi K3 leads on SWE-bench coding (74.5% vs 74.0%).
| Feature | Llama 5 | Kimi K3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 94 | 93 |
Provider | Meta | Moonshot AI |
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 | 5M tokens~7,500 A4 pages of text | 1.05M tokens~1,573 A4 pages of text |
Max Output Tokens Per single request | 128K | 131K |
Throughput Speed Tokens generated per second | 65 tok/s | 58 tok/s |
SWE-bench Verified Real-world software engineering | 74% | 74.5% |
GPQA Diamond Graduate-level reasoning | 84% | 79% |
分野別勝者スコアカード
At-a-glance decision framework検証済みベンチマークスコア比較
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.
Tokens emitted per second during active generation streaming.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 5 · Strengths & Key Advantages
- Meta の高度な Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) アーキテクチャと 5M トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0/1M、出力 $0/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Kimi K3 · Strengths & Key Advantages
- Moonshot AI の高度な Colossus-Scale Multimodal Sparse Mixture-of-Experts アーキテクチャと 1.0M トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0/1M、出力 $0/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 5 | Kimi K3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $2.00 |
Output Tokens / Task Tokens generated per task | 40K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 20K | 18K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 5 | Kimi K3 |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-5 | kimi-k3 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-04 | 2026-07 |
| Knowledge Cutoff | January 2026 | March 2026 |
| Architecture | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) | Colossus-Scale Multimodal Sparse Mixture-of-Experts |
| Parameters | 600B+ total (~40-80B active) | 2.8T total (MoE) |
| 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) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 372 GB VRAM (4-bit Quantized) · 1290 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1736 GB VRAM (4-bit Quantized) · 6020 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, video, audio | text, vision |
| Output Modalities | text | text |
Llama 5 vs Kimi K3: よくある質問 (FAQ)
Which is more intelligent, Llama 5 or Kimi K3?
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 58 tokens per second.
Which model has lower latency?
Kimi K3 has lower latency, with a time to first token of 0.72s compared with 0.85s.
Which model has a larger context window?
Llama 5 has a larger context window, supporting 5M tokens compared with 1.05M tokens.
総合評価
Verdict & Model Selection Recommendation: Choose Llama 5 if your priority is Autonomous open-source multi-agent systems, deep software refactoring, and frontier research. Its performance in demanding tasks justifies its operational token cost. Choose Kimi K3 if your workflow prioritizes National AI research facilities, sovereign enterprise clouds, and frontier STEM reasoning. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
よくある質問 (FAQ)
How does Llama 5 compare to Kimi K3 in API token pricing and cost per token?+
Both Llama 5 and Kimi K3 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 5 or Kimi K3?+
Llama 5 supports a context window of 5,000K tokens (~3,750k words), while Kimi K3 supports 1,049K tokens (~786k words). Therefore, Llama 5 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Llama 5 or Kimi K3?+
Llama 5 scores 74% on SWE-bench Verified while Kimi K3 scores 74.5%. For autonomous software engineering and multi-file debugging, Kimi K3 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Llama 5 and Kimi K3?+
Llama 5 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Kimi K3 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Llama 5 and Kimi K3?+
Llama 5 generates approximately 65 tokens per second, while Kimi K3 delivers 58 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 5 and Kimi K3 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 5. 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.
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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.
公的ソース: GetInfoUs Independent AI Benchmarks & Official API Specs