Head-to-Head Model Comparison

Claude 3.5 Sonnet vs GPT-6 Astra

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado por el equipo de investigación de GetInfoUsÚltima revisión: October 2026Política editorial
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Compare Claude 3.5 Sonnet by Anthropic against GPT-6 Astra by OpenAI across real-world coding, reasoning, token economics, and latency.

Comparación Directa (VS)

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Anthropic

Claude 3.5 Sonnet

Rey de la codificación en la industria

El aclamado rey de los puntos de referencia de ingeniería de software que transformó la industria con codificación multifarchivo sin rival, autonomía agéntica y Artifacts.

Intelligence Index50 / 100
Input Token:

$3.00 / 1M

Output Token:

$15.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

65%

OpenAI

GPT-6 Astra

Agente de SO autónomo

de OpenAI late-2026 insignia modelo fundacional con nativo uso de computadora agentes, dynamic UI orchestration, y profundo STEM razonamiento.

Intelligence Index96 / 100
Input Token:

$10.00 / 1M

Output Token:

$50.00 / 1M

Context Window:

272K tokens

SWE-bench Verified:

82.4%

Diferencias Clave de un Vistazo

  • Claude 3.5 Sonnet is 3.3x cheaper for API usage (input + output tokens combined).
  • GPT-6 Astra has a larger context window (272K vs 200K tokens).
  • GPT-6 Astra is faster at 100 tok/s vs 80 tok/s.
  • GPT-6 Astra leads on SWE-bench coding (82.4% vs 65.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
CaracterísticaClaude 3.5 SonnetGPT-6 Astra
Intelligence Index
Composite reasoning & knowledge score (0-100)
5096
Provider
AnthropicOpenAI
Input Price
Cost per million input tokens
$3.00 / 1M$10.00 / 1M
Output Price
Cost per million output tokens
$15.00 / 1M$50.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$2.31 / 1M$7.70 / 1M
Context Window
Maximum input + output tokens
200K tokens~300 A4 pages of text272K tokens~408 A4 pages of text
Max Output Tokens
Per single request
8K128K
Throughput Speed
Tokens generated per second
80 tok/s100 tok/s
SWE-bench Verified
Real-world software engineering
65%82.4%
GPQA Diamond
Graduate-level reasoning
59.4%80.9%
Cached Input Price
Prompt caching discount per 1M tokens
$0.30 / 1M$1.00 / 1M

¿Quién gana en cada área?

At-a-glance decision framework
Código
GPT-6 Astra
+17.4% SWE-bench lead
Costo
Claude 3.5 Sonnet
3.3x cheaper monthly runtime
Velocidad
GPT-6 Astra
+25% faster generation
Contexto
GPT-6 Astra
272K tokens window

Puntuaciones de Benchmarks Verificadas

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

Claude 3.5 SonnetGPT-6 Astra
SWE-bench Verified (Autonomous Coding)

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

65%vs82.4%
GPQA Diamond (Doctorate-Level Science & Math)

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

59.4%vs80.9%
MMLU-Pro (Multi-task Language Understanding)

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

78%vs89.9%
LMSYS Chatbot Arena Elo

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

1320ptsvs1520pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs18.2%
Terminal-Bench (Agentic Coding & Terminal Use)

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

38%vs63.8%
SciCode (Scientific Computing)

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

N/Avs54.9%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

62%vs79.8%
AIME (Advanced Math Competition)

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

N/Avs93.6%
Long-Context Reasoning

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

N/Avs91.2%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

80 tok/s vs100 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
Claude 3.5 Sonnet0.48s
GPT-6 Astra0.95s
Est. End-to-End Response (500 tokens)TTFT + generation time
Claude 3.5 Sonnet6.73s
GPT-6 Astra5.95s

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)
Claude 3.5 Sonnet Monthly API Bill
$18.45 / month
$3/1M in · $15/1M out
GPT-6 Astra Monthly API Bill
$61.50 / month
$10/1M in · $50/1M out
Claude 3.5 Sonnet saves approximately $43.05 / month (70%) under this workload.3.3x Savings

Claude 3.5 Sonnet · Strengths & Key Advantages

  • Widely regarded as el líder modelo para completo-stack programación y code refactorización
  • Pioneered interactivo frontend Artifacts y autónomo Computer Use capacidades
  • 90% costo ahorros en repeated prefixes via nativo prompt caching
Considerations & Trade-offs
  • Pre-dated nativo extendido pensamiento cadena de pensamiento toggles en Claude 3.7 Sonnet
  • ventana de contexto capped un 200K (smaller que Gemini 2M)

GPT-6 Astra · Strengths & Key Advantages

  • Flawless OS-level navigation y uso de computadora automatización
  • Seamless en tiempo real multimodal voz y visión interactivity
  • Dynamic cadena de pensamiento que self-adjusts profundidad based en query difficulty
  • enorme 1M ventana de contexto con instant prefix caching
Considerations & Trade-offs
  • Strict seguridad filters puede occasionally throttle complejo penetration testing prompts
  • Slightly mayor razonamiento latencia en alto-effort matemáticas queries

Token Use & Task Economics

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

MetricClaude 3.5 SonnetGPT-6 Astra
Cost per Task
Estimated USD to finish one task
$0.20$2.15
Output Tokens / Task
Tokens generated per task
24K58K
Reasoning Tokens / Task
Thinking tokens burned per task
0K32K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

CaracterísticaClaude 3.5 SonnetGPT-6 Astra
Official API Model ID (SDK)claude-3-5-sonnet-20240601gpt-6-astra
API ProtocolAnthropic Messages API + AWS Bedrock + Vertex AIOpenAI Responses API & Chat Completions v1
Release Date2024-062026-09
Knowledge CutoffApril 2024June 2026
ArchitectureNext-Gen Multimodal Hybrid TransformerOmni-Adaptive Autoregressive with Dynamic CoT
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 800,000 – 2,000,000 TPM10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)Standard rate up to 200K tokens; 2x input/output tier above 200K tokensFlat rate ($10/1M in) across full 272K window
Prompt Cache Policy & TTL$0.3/1M cached read (90% off); 5-min default & 1-hour extended TTLAutomatic prefix caching at $1/1M (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 Citations + Code Execution Tool + MCP ConnectorsNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportCustom Model Tuning via Amazon Bedrock & Prompt DistillationSupervised 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) · Audio (~25–32 tokens/sec)
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 ClassificationAnthropic RSP ASL-2 / ASL-3 Constitutional Safety EvaluatedOpenAI 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, audio
Output Modalitiestexttext, audio

Claude 3.5 Sonnet vs GPT-6 Astra: Respuestas Rápidas (FAQ)

Which is more intelligent, Claude 3.5 Sonnet or GPT-6 Astra?

GPT-6 Astra is more intelligent. It scores 96 compared with 50 on the composite Intelligence Index.

Which model is faster?

GPT-6 Astra is faster, generating 100 tokens per second compared with 80 tokens per second.

Which model is cheaper to run?

Claude 3.5 Sonnet is cheaper at $2.31 per 1M tokens blended, compared with $7.70 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Claude 3.5 Sonnet has lower latency, with a time to first token of 0.48s compared with 0.95s.

Which model has a larger context window?

GPT-6 Astra has a larger context window, supporting 272K tokens compared with 200K tokens.

Veredicto

Choose Claude 3.5 Sonnet for ingeniería de software multifarchivo compleja, agentes de desarrollo, creación de prototipos visuales de ui y uso autónomo de computadora, or choose GPT-6 Astra for autónomo browser/computer flujos de trabajo, en tiempo real voz agentes, y alto-difficulty matemático razonamiento.

Preguntas frecuentes

Which is cheaper: Claude 3.5 Sonnet or GPT-6 Astra?+

Claude 3.5 Sonnet costs $3/1M input and $15/1M output tokens, while GPT-6 Astra costs $10/1M input and $50/1M output tokens.

Which model has a larger context window: Claude 3.5 Sonnet or GPT-6 Astra?+

Claude 3.5 Sonnet supports 200K tokens whereas GPT-6 Astra supports 272K tokens.

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: Anthropic & OpenAI Official Documentation