Grok 4.20 (Reasoning) vs o3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Grok 4.20 (Reasoning) by xAI and o3 by OpenAI 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, o3 holds a notable pricing advantage for token ingestion. On output generation, o3 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Grok 4.20 (Reasoning) provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, Grok 4.20 (Reasoning) 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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Grok 4.20 (Reasoning)
高度な推論モデルxAI が提供する Grok 4.20 (Reasoning) は、最大 1M トークンのコンテキストウィンドウを備え、入力 $1.25/100万トークン・出力 $2.5/100万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。
$3.00 / 1M
$15.00 / 1M
1M tokens
68.2%
o3
高度な推論モデルOpenAI が提供する o3 は、最大 200K トークンのコンテキストウィンドウを備え、入力 $2/100万トークン・出力 $8/100万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。
$2.00 / 1M
$8.00 / 1M
200K tokens
69.1%
ひと目でわかる主な違い
- o3 is 1.7x cheaper for API usage (input + output tokens combined).
- Grok 4.20 (Reasoning) has a larger context window (1M vs 200K tokens).
- Grok 4.20 (Reasoning) is faster at 95 tok/s vs 75 tok/s.
- o3 leads on SWE-bench coding (69.1% vs 68.2%).
| Feature | Grok 4.20 (Reasoning) | o3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 68 | 56 |
Provider | xAI | OpenAI |
Input Price Cost per million input tokens | $3.00 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $15.00 / 1M | $8.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $2.63 / 1M | $1.55 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 200K tokens~300 A4 pages of text |
Max Output Tokens Per single request | 66K | 100K |
Throughput Speed Tokens generated per second | 95 tok/s | 75 tok/s |
SWE-bench Verified Real-world software engineering | 68.2% | 69.1% |
GPQA Diamond Graduate-level reasoning | 72.8% | 76.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.75 / 1M | $0.50 / 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.
Grok 4.20 (Reasoning) · Strengths & Key Advantages
- xAI の高度な Colossus-Scale Reasoning Sparse MoE アーキテクチャと 1M 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 $1.25/1M、出力 $2.5/1M）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
o3 · Strengths & Key Advantages
- OpenAI の高度な Reasoning transformer with adjustable thinking effort アーキテクチャと 200K 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 $2/1M、出力 $8/1M）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 | Grok 4.20 (Reasoning) | o3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.45 | $1.35 |
Output Tokens / Task Tokens generated per task | 45K | 48K |
Reasoning Tokens / Task Thinking tokens burned per task | 28K | 26K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Grok 4.20 (Reasoning) | o3 |
|---|---|---|
| Official API Model ID (SDK) | grok-4-20-reasoning-latest | o3 |
| API Protocol | xAI REST API (OpenAI-Compatible) | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-03 | 2025-04 |
| Knowledge Cutoff | November 2025 | June 2024 |
| Architecture | Colossus-Scale Reasoning Sparse MoE | Reasoning transformer with adjustable thinking effort |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($3/1M in) across full 1000K window | Flat rate ($2/1M in) across full 200K window |
| Prompt Cache Policy & TTL | Cached input at $0.75/1M tokens | Automatic prefix caching at $0.5/1M (no extra cache-write fee) |
| 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 | Real-time Web & X (Twitter) Search Grounding + Code Execution | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| 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 | xAI Production Safety & Alignment Verified | OpenAI Preparedness Framework Audited & System Card Published |
| 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 |
Grok 4.20 (Reasoning) vs o3: よくある質問 (FAQ)
Which is more intelligent, Grok 4.20 (Reasoning) or o3?
Grok 4.20 (Reasoning) is more intelligent. It scores 68 compared with 56 on the composite Intelligence Index.
Which model is faster?
Grok 4.20 (Reasoning) is faster, generating 95 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
o3 is cheaper at $1.55 per 1M tokens blended, compared with $2.63 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Grok 4.20 (Reasoning) has lower latency, with a time to first token of 0.98s compared with 2.80s.
Which model has a larger context window?
Grok 4.20 (Reasoning) has a larger context window, supporting 1M tokens compared with 200K tokens.
総合評価
Verdict & Model Selection Recommendation: Choose Grok 4.20 (Reasoning) if your priority is Deep document analysis, multi-stage enterprise research, and extensive tool-driven workflows. Its performance in demanding tasks justifies its operational token cost. Choose o3 if your workflow prioritizes Hard maths, science and agentic coding where accuracy beats latency. 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 Grok 4.20 (Reasoning) compare to o3 in API token pricing and cost per token?+
Grok 4.20 (Reasoning) costs $3.00 per 1M input tokens and $15.00 per 1M output tokens, whereas o3 costs $2.00 input and $8.00 output. Overall, o3 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: Grok 4.20 (Reasoning) or o3?+
Grok 4.20 (Reasoning) supports a context window of 1,000K tokens (~750k words), while o3 supports 200K tokens (~150k words). Therefore, Grok 4.20 (Reasoning) can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Grok 4.20 (Reasoning) or o3?+
Grok 4.20 (Reasoning) scores 68.2% on SWE-bench Verified while o3 scores 69.1%. For autonomous software engineering and multi-file debugging, o3 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Grok 4.20 (Reasoning) and o3?+
Grok 4.20 (Reasoning) features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, o3 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Grok 4.20 (Reasoning) and o3?+
Grok 4.20 (Reasoning) generates approximately 95 tokens per second, while o3 delivers 75 tokens per second. In latency-sensitive production environments, Grok 4.20 (Reasoning) provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Grok 4.20 (Reasoning) and o3 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to o3 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Grok 4.20 (Reasoning). 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.
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