GPT-OSS 120B vs Llama 4 Behemoth
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT-OSS 120B by OpenAI against Llama 4 Behemoth by Meta across real-world coding, reasoning, token economics, and latency.
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GPT-OSS 120B
オープンウェイトモデルOpenAI が提供する GPT-OSS 120B は、最大 131K トークンのコンテキストウィンドウを備え、入力 $0.15/100万トークン・出力 $0.6/100万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。
$0.15 / 1M
$0.60 / 1M
131.1K tokens
45.5%
Llama 4 Behemoth
オープンウェイトモデルMeta が提供する Llama 4 Behemoth は、最大 10M 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
10M tokens
75%
ひと目でわかる主な違い
- Llama 4 Behemoth is 1.0x cheaper for API usage (input + output tokens combined).
- Llama 4 Behemoth has a larger context window (10M vs 131K tokens).
- GPT-OSS 120B is faster at 95 tok/s vs 45 tok/s.
- Llama 4 Behemoth leads on SWE-bench coding (75.0% vs 45.5%).
| Feature | GPT-OSS 120B | Llama 4 Behemoth |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 56 | 93 |
Provider | OpenAI | Meta |
Input Price Cost per million input tokens | $0.15 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.60 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.10 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 131.1K tokens~197 A4 pages of text | 10M tokens~15,000 A4 pages of text |
Max Output Tokens Per single request | 33K | 64K |
Throughput Speed Tokens generated per second | 95 tok/s | 45 tok/s |
SWE-bench Verified Real-world software engineering | 45.5% | 75% |
GPQA Diamond Graduate-level reasoning | 68.5% | 82% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.00 / 1M |
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.
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.
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.
GPT-OSS 120B · Strengths & Key Advantages
- OpenAI の高度な Sparse MoE transformer with reasoning アーキテクチャと 131K 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 $0.15/1M、出力 $0.6/1M）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
Llama 4 Behemoth · Strengths & Key Advantages
- Meta の高度な Colossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE) アーキテクチャと 10M 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 | GPT-OSS 120B | Llama 4 Behemoth |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.14 | $0.00 |
Output Tokens / Task Tokens generated per task | 45K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 22K | 15K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-OSS 120B | Llama 4 Behemoth |
|---|---|---|
| Official API Model ID (SDK) | gpt-oss-120b | meta-llama/Llama-4-Behemoth |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-08 | 2026-05 |
| Knowledge Cutoff | June 2024 | February 2026 |
| Architecture | Sparse MoE transformer with reasoning | Colossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE) |
| Parameters | 117B (5.1B active MoE) | 2T total / 288B active (16-of-2 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) | Flat rate ($0.15/1M in) across full 131K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.015/1M (no extra cache-write fee) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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 | 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) | 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) · 73 GB VRAM (4-bit Quantized) · 252 GB VRAM (Native BF16) | 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) | 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 | text, vision |
| Output Modalities | text | text |
GPT-OSS 120B vs Llama 4 Behemoth: よくある質問 (FAQ)
Which is more intelligent, GPT-OSS 120B or Llama 4 Behemoth?
Llama 4 Behemoth is more intelligent. It scores 93 compared with 56 on the composite Intelligence Index.
Which model is faster?
GPT-OSS 120B is faster, generating 95 tokens per second compared with 45 tokens per second.
Which model is cheaper to run?
Llama 4 Behemoth is cheaper at $0.00 per 1M tokens blended, compared with $0.10 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-OSS 120B has lower latency, with a time to first token of 0.48s compared with 1.20s.
Which model has a larger context window?
Llama 4 Behemoth has a larger context window, supporting 10M tokens compared with 131.1K tokens.
総合評価
Choose GPT-OSS 120B for エンタープライズ自動化、ソフトウェア開発、ai エージェント構築、openai エコシステムの統合, or choose Llama 4 Behemoth for エンタープライズ自動化、ソフトウェア開発、ai エージェント構築、meta エコシステムの統合.
よくある質問 (FAQ)
Which is cheaper: GPT-OSS 120B or Llama 4 Behemoth?+
GPT-OSS 120B costs $0.15/1M input and $0.6/1M output tokens, while Llama 4 Behemoth costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: GPT-OSS 120B or Llama 4 Behemoth?+
GPT-OSS 120B supports 131K 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.
公的ソース: OpenAI & Meta Official Documentation