GPT-5.2 Codex vs GPT-5.1 Codex Max
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GPT-5.2 Codex by OpenAI and GPT-5.1 Codex Max 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, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 400K tokens. In latency-sensitive production environments, GPT-5.2 Codex 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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GPT-5.2 Codex
Deep-Reasoning-ModellGPT-5.2 Codex von OpenAI ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 400K Token zu API-Preisen von $1.75/1M Input- und $14/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$1.75 / 1M
$14.00 / 1M
400K tokens
78.1%
GPT-5.1 Codex Max
Deep-Reasoning-ModellGPT-5.1 Codex Max von OpenAI ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 400K Token zu API-Preisen von $1.75/1M Input- und $14/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$1.75 / 1M
$14.00 / 1M
400K tokens
78.2%
Wichtigste Unterschiede auf einen Blick
- Both models cost the same under this workload — choose on capability, not price.
- GPT-5.2 Codex is faster at 118 tok/s vs 95 tok/s.
- GPT-5.1 Codex Max leads on SWE-bench coding (78.2% vs 78.1%).
| Feature | GPT-5.2 Codex | GPT-5.1 Codex Max |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 66 | 62 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $1.75 / 1M | $1.75 / 1M |
Output Price Cost per million output tokens | $14.00 / 1M | $14.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.87 / 1M | $1.87 / 1M |
Context Window Maximum input + output tokens | 400K tokens~600 A4 pages of text | 400K tokens~600 A4 pages of text |
Max Output Tokens Per single request | 128K | 128K |
Throughput Speed Tokens generated per second | 118 tok/s | 95 tok/s |
SWE-bench Verified Real-world software engineering | 78.1% | 78.2% |
GPQA Diamond Graduate-level reasoning | 78.2% | 80% |
Cached Input Price Prompt caching discount per 1M tokens | $0.17 / 1M | $0.17 / 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.
GPT-5.2 Codex · Strengths & Key Advantages
- Fortschrittliche Code-specialised long-horizon reasoning model-Architektur von OpenAI mit 400K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($1.75/1M Input, $14/1M 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
GPT-5.1 Codex Max · Strengths & Key Advantages
- Fortschrittliche Maximum-capability code-specialised reasoning model-Architektur von OpenAI mit 400K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($1.75/1M Input, $14/1M 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 | GPT-5.2 Codex | GPT-5.1 Codex Max |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.20 | $2.40 |
Output Tokens / Task Tokens generated per task | 60K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 34K | 36K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-5.2 Codex | GPT-5.1 Codex Max |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-2-codex | gpt-5-1-codex-max |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-12 | 2025-11 |
| Knowledge Cutoff | August 2025 | October 2024 |
| Architecture | Code-specialised long-horizon reasoning model | Maximum-capability code-specialised reasoning model |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($1.75/1M in) across full 400K window | Flat rate ($1.75/1M in) across full 400K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.175/1M (no extra cache-write fee) | Automatic prefix caching at $0.175/1M (no extra cache-write fee) |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | 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 | OpenAI Preparedness Framework Audited & System Card Published | 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 |
GPT-5.2 Codex vs GPT-5.1 Codex Max: Schnelle Antworten (FAQ)
Which is more intelligent, GPT-5.2 Codex or GPT-5.1 Codex Max?
GPT-5.2 Codex is more intelligent. It scores 66 compared with 62 on the composite Intelligence Index.
Which model is faster?
GPT-5.2 Codex is faster, generating 118 tokens per second compared with 95 tokens per second.
Which model has lower latency?
GPT-5.2 Codex has lower latency, with a time to first token of 1.28s compared with 1.70s.
Fazit
Verdict & Model Selection Recommendation: Choose GPT-5.2 Codex if your priority is Autonomous software agents and large migrations. Its performance in demanding tasks justifies its operational token cost. Choose GPT-5.1 Codex Max if your workflow prioritizes Enterprise-scale code agents and monorepo work. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Häufig gestellte Fragen (FAQ)
How does GPT-5.2 Codex compare to GPT-5.1 Codex Max in API token pricing and cost per token?+
Both GPT-5.2 Codex and GPT-5.1 Codex Max share matched API token pricing at $1.75 per 1M input tokens and $14.00 per 1M output tokens. Because their token economics are identical, your selection should be guided by benchmark latency, memory horizon, and reasoning capabilities rather than operational token fees. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: GPT-5.2 Codex or GPT-5.1 Codex Max?+
Both GPT-5.2 Codex and GPT-5.1 Codex Max offer an identical context window of 400K tokens (~300k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: GPT-5.2 Codex or GPT-5.1 Codex Max?+
GPT-5.2 Codex scores 78.1% on SWE-bench Verified while GPT-5.1 Codex Max scores 78.2%. For autonomous software engineering and multi-file debugging, GPT-5.1 Codex Max holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GPT-5.2 Codex and GPT-5.1 Codex Max?+
GPT-5.2 Codex features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT-5.1 Codex Max provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GPT-5.2 Codex and GPT-5.1 Codex Max?+
GPT-5.2 Codex generates approximately 118 tokens per second, while GPT-5.1 Codex Max delivers 95 tokens per second. In latency-sensitive production environments, GPT-5.2 Codex provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-5.2 Codex and GPT-5.1 Codex Max 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 GPT-5.2 Codex, and route deep architectural or complex reasoning queries to GPT-5.2 Codex. 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.
Offizielle Quellen: GetInfoUs Independent AI Benchmarks & Official API Specs