Head-to-Head Model Comparison

GPT-5.1 Codex vs GPT-5.1 Codex mini

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado por el equipo de investigación de GetInfoUsÚltima revisión: October 2026Política editorial
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
GPT-5.1 Codex vs GPT-5.1 Codex mini: Benchmarks, Pricing & Speed Comparison

GPT-5.1 Codex by OpenAI and GPT-5.1 Codex mini 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, GPT-5.1 Codex mini holds a notable pricing advantage for token ingestion. On output generation, GPT-5.1 Codex mini is more economical per million generated tokens. 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-5.1 Codex mini 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.

Comparación Directa (VS)

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GPT-5.1 Codex

Actualización de Codex

el Codex line upgraded un GPT-5.1 para repositorio edits, code revisión y real software agentes.

Intelligence Index61 / 100
Input Token:

$1.25 / 1M

Output Token:

$10.00 / 1M

Context Window:

400K tokens

SWE-bench Verified:

76.1%

OpenAI

GPT-5.1 Codex mini

Codex compacto

un cheap codificación-optimised Codex lane para rápido edits y revisión un escala.

Intelligence Index47 / 100
Input Token:

$0.25 / 1M

Output Token:

$2.00 / 1M

Context Window:

400K tokens

SWE-bench Verified:

55.5%

Diferencias Clave de un Vistazo

  • GPT-5.1 Codex mini is 5.0x cheaper for API usage (input + output tokens combined).
  • GPT-5.1 Codex mini is faster at 180 tok/s vs 125 tok/s.
  • GPT-5.1 Codex leads on SWE-bench coding (76.1% vs 55.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
CaracterísticaGPT-5.1 CodexGPT-5.1 Codex mini
Intelligence Index
Composite reasoning & knowledge score (0-100)
6147
Provider
OpenAIOpenAI
Input Price
Cost per million input tokens
$1.25 / 1M$0.25 / 1M
Output Price
Cost per million output tokens
$10.00 / 1M$2.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$1.34 / 1M$0.27 / 1M
Context Window
Maximum input + output tokens
400K tokens~600 A4 pages of text400K tokens~600 A4 pages of text
Max Output Tokens
Per single request
128K128K
Throughput Speed
Tokens generated per second
125 tok/s180 tok/s
SWE-bench Verified
Real-world software engineering
76.1%55.5%
GPQA Diamond
Graduate-level reasoning
78.5%66.5%
Cached Input Price
Prompt caching discount per 1M tokens
$0.13 / 1M$0.03 / 1M

¿Quién gana en cada área?

At-a-glance decision framework
Código
GPT-5.1 Codex
+20.6% SWE-bench lead
Costo
GPT-5.1 Codex mini
5.0x cheaper monthly runtime
Velocidad
GPT-5.1 Codex mini
+44% faster generation
Contexto
Tied
400K tokens window

Puntuaciones de Benchmarks Verificadas

Official evaluation metrics verified by independent third-party research labs.

GPT-5.1 CodexGPT-5.1 Codex mini
SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

76.1%vs55.5%
GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

78.5%vs66.5%
MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

86.5%vs79%
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1372ptsvs1320pts
Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

12.6%vs9.4%
Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

52%vs32%
SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

46.5%vs33%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

74%vs58%
AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

91.8%vs84.5%
Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

78.5%vs69%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

125 tok/s vs180 tok/s

Latencia y Tiempo de Respuesta

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better
GPT-5.1 Codex1.20s
GPT-5.1 Codex mini0.72s
Est. End-to-End Response (500 tokens)TTFT + generation time
GPT-5.1 Codex5.20s
GPT-5.1 Codex mini3.50s

Simulador Interactivo de Costos de Tokens

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
GPT-5.1 Codex Monthly API Bill
$11.06 / month
$1.25/1M in · $10/1M out
GPT-5.1 Codex mini Monthly API Bill
$2.21 / month
$0.25/1M in · $2/1M out
GPT-5.1 Codex mini saves approximately $8.85 / month (80%) under this workload.5.0x Savings

GPT-5.1 Codex · Strengths & Key Advantages

  • Clear improvement sobre GPT-5-Codex en repositorio tareas
  • Gestiona largas sesiones de edición agéntica con fiabilidad
  • Priced identically un el previous Codex generación
  • mejor un cross-file consistency que GPT-5-Codex
Considerations & Trade-offs
  • Premium precios para non-codificación use cases
  • Gains sobre GPT-5-Codex son incremental rather que dramatic

GPT-5.1 Codex mini · Strengths & Key Advantages

  • Cheap, rápido codificación assistance
  • bueno para lint fixes, refactors y revisión comments
  • Retains el 400K ventana de contexto
  • excelente para linting, formateo y pequeño refactors
Considerations & Trade-offs
  • Struggles en grande, interdependent repositories
  • menos confiable que completo Codex en multifarchivo refactors

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricGPT-5.1 CodexGPT-5.1 Codex mini
Cost per Task
Estimated USD to finish one task
$1.65$0.33
Output Tokens / Task
Tokens generated per task
58K48K
Reasoning Tokens / Task
Thinking tokens burned per task
33K23K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

CaracterísticaGPT-5.1 CodexGPT-5.1 Codex mini
Official API Model ID (SDK)gpt-5-1-codexgpt-5-1-codex-mini
API ProtocolOpenAI Responses API & Chat Completions v1OpenAI Responses API & Chat Completions v1
Release Date2025-112025-11
Knowledge CutoffOctober 2024October 2024
ArchitectureCode-specialised unified reasoning modelCompact code-specialised reasoning model
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($1.25/1M in) across full 400K windowFlat rate ($0.25/1M in) across full 400K window
Prompt Cache Policy & TTLAutomatic prefix caching at $0.125/1M (no extra cache-write fee)Automatic prefix caching at $0.025/1M (no extra cache-write fee)
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxNative Web Search tool + Hosted Code Interpreter & File Search RAGNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token BillingText 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 VRAMCloud-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 ClassificationOpenAI Preparedness Framework Audited & System Card PublishedOpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestext, visiontext, vision
Output Modalitiestexttext

GPT-5.1 Codex vs GPT-5.1 Codex mini: Respuestas Rápidas (FAQ)

Which is more intelligent, GPT-5.1 Codex or GPT-5.1 Codex mini?

GPT-5.1 Codex is more intelligent. It scores 61 compared with 47 on the composite Intelligence Index.

Which model is faster?

GPT-5.1 Codex mini is faster, generating 180 tokens per second compared with 125 tokens per second.

Which model is cheaper to run?

GPT-5.1 Codex mini is cheaper at $0.27 per 1M tokens blended, compared with $1.34 (7:2:1 cache:input:output ratio).

Which model has lower latency?

GPT-5.1 Codex mini has lower latency, with a time to first token of 0.72s compared with 1.20s.

Veredicto

Verdict & Model Selection Recommendation: Choose GPT-5.1 Codex if your priority is Code-centric product teams running agentic development. Its performance in demanding tasks justifies its operational token cost. Choose GPT-5.1 Codex mini if your workflow prioritizes High-volume code automation and inline review. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.

Preguntas frecuentes

How does GPT-5.1 Codex compare to GPT-5.1 Codex mini in API token pricing and cost per token?+

GPT-5.1 Codex costs $1.25 per 1M input tokens and $10.00 per 1M output tokens, whereas GPT-5.1 Codex mini costs $0.25 input and $2.00 output. Overall, GPT-5.1 Codex mini 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: GPT-5.1 Codex or GPT-5.1 Codex mini?+

Both GPT-5.1 Codex and GPT-5.1 Codex mini 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-5.1 Codex or GPT-5.1 Codex mini?+

GPT-5.1 Codex scores 76.1% on SWE-bench Verified while GPT-5.1 Codex mini scores 55.5%. For autonomous software engineering and multi-file debugging, GPT-5.1 Codex holds the verified benchmark lead.

What is the difference in reasoning capability and extended thinking between GPT-5.1 Codex and GPT-5.1 Codex mini?+

GPT-5.1 Codex features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT-5.1 Codex mini provides chain-of-thought reasoning depth for complex math and science.

How does inference speed (tokens per second) compare between GPT-5.1 Codex and GPT-5.1 Codex mini?+

GPT-5.1 Codex generates approximately 125 tokens per second, while GPT-5.1 Codex mini delivers 180 tokens per second. In latency-sensitive production environments, GPT-5.1 Codex mini provides faster time-to-first-token (TTFT) and higher streaming response velocity.

Can you implement a tiered LLM routing setup combining GPT-5.1 Codex and GPT-5.1 Codex mini to optimize costs?+

Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-5.1 Codex mini to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-5.1 Codex. This tiered routing pattern cuts overall API bills by up to 70%.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.

Fuentes: GetInfoUs Independent AI Benchmarks & Official API Specs