Head-to-Head Model Comparison

Claude Haiku 5.5 vs GPT-6.1 Sol

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado por el equipo de investigación de GetInfoUsÚltima revisión: October 2026Política editorial
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AnthropicOpenAIlightweightflagship

Compare Claude Haiku 5.5 by Anthropic against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.

Comparación Directa (VS)

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Claude Haiku 5.5

Vía rápida 5.5

el ultra-rápido, bajo-costo potencia de late 2026, bringing rápido razonamiento, 200K contexto, y near-instant response speeds un cliente agentes y en tiempo real canalizaciones.

Intelligence Index62 / 100
Input Token:

$1.00 / 1M

Output Token:

$5.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

68.4%

OpenAI

GPT-6.1 Sol

Valor cercano a Astra

ofrece results close un GPT-6 Astra un roughly one fifth del precio — el latest 2026 lanzamiento.

Intelligence Index94 / 100
Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

82.3%

Diferencias Clave de un Vistazo

  • Claude Haiku 5.5 is 2.0x cheaper for API usage (input + output tokens combined).
  • GPT-6.1 Sol has a larger context window (1M vs 200K tokens).
  • Claude Haiku 5.5 is faster at 160 tok/s vs 120 tok/s.
  • GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 68.4%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
CaracterísticaClaude Haiku 5.5GPT-6.1 Sol
Intelligence Index
Composite reasoning & knowledge score (0-100)
6294
Provider
AnthropicOpenAI
Input Price
Cost per million input tokens
$1.00 / 1M$2.00 / 1M
Output Price
Cost per million output tokens
$5.00 / 1M$10.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.77 / 1M$1.47 / 1M
Context Window
Maximum input + output tokens
200K tokens~300 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
64K128K
Throughput Speed
Tokens generated per second
160 tok/s120 tok/s
SWE-bench Verified
Real-world software engineering
68.4%82.3%
GPQA Diamond
Graduate-level reasoning
69.8%80.8%
Cached Input Price
Prompt caching discount per 1M tokens
$0.10 / 1M$0.10 / 1M

¿Quién gana en cada área?

At-a-glance decision framework
Código
GPT-6.1 Sol
+13.9% SWE-bench lead
Costo
Claude Haiku 5.5
2.0x cheaper monthly runtime
Velocidad
Claude Haiku 5.5
+33% faster generation
Contexto
GPT-6.1 Sol
1M tokens window

Puntuaciones de Benchmarks Verificadas

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

Claude Haiku 5.5GPT-6.1 Sol
SWE-bench Verified (Autonomous Coding)

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

68.4%vs82.3%
GPQA Diamond (Doctorate-Level Science & Math)

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

69.8%vs80.8%
MMLU-Pro (Multi-task Language Understanding)

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

83.2%vs89.9%
LMSYS Chatbot Arena Elo

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

1370ptsvs1519pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs18.1%
Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs63.6%
SciCode (Scientific Computing)

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

N/Avs54.8%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs79.7%
AIME (Advanced Math Competition)

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

N/Avs93.5%
Long-Context Reasoning

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

N/Avs91%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

160 tok/s vs120 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 Haiku 5.50.22s
GPT-6.1 Sol1.12s
Est. End-to-End Response (500 tokens)TTFT + generation time
Claude Haiku 5.53.35s
GPT-6.1 Sol5.29s

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 Haiku 5.5 Monthly API Bill
$6.15 / month
$1/1M in · $5/1M out
GPT-6.1 Sol Monthly API Bill
$12.15 / month
$2/1M in · $10/1M out
Claude Haiku 5.5 saves approximately $6.00 / month (49%) under this workload.2.0x Savings

Claude Haiku 5.5 · Strengths & Key Advantages

  • Blazing 160 tokens/sec rendimiento con ultra-bajo 220ms tiempo-un-primer-token
  • Cheap $1.00 / $5.00 API precios con $0.10 cached inputs
  • Integrated razonamiento modo ofreciendo mayor precisión en triaje tareas
Considerations & Trade-offs
  • ventana de contexto limitado un 200K tokens rather que el 1M de Sonnet/Opus
  • no intended para autónomo multifarchivo arquitectura redesigns

GPT-6.1 Sol · Strengths & Key Advantages

  • Near-Astra results un roughly un fifth del costo
  • Excellent codificación y de largo alcance agente rendimiento
  • sólido reliability con far menor latencia que Astra
  • Near-insignia results un roughly un fifth del precio
Considerations & Trade-offs
  • Still marginally behind Astra en el muy hardest razonamiento
  • Same precio as GPT-6 Sol, so el upgrade decision es easy

Token Use & Task Economics

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

MetricClaude Haiku 5.5GPT-6.1 Sol
Cost per Task
Estimated USD to finish one task
$0.70$2.05
Output Tokens / Task
Tokens generated per task
20K62K
Reasoning Tokens / Task
Thinking tokens burned per task
10K36K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

CaracterísticaClaude Haiku 5.5GPT-6.1 Sol
Official API Model ID (SDK)claude-haiku-5-5-20261001gpt-6-1-sol
API ProtocolAnthropic Messages API + AWS Bedrock + Vertex AIOpenAI Responses API & Chat Completions v1
Release Date2026-102026-09
Knowledge CutoffAugust 2026April 2026
ArchitectureUltra-Fast Lightweight Reasoning TransformerNear-Astra unified reasoning model
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) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Standard rate up to 200K tokens; 2x input/output tier above 200K tokensFlat rate ($2/1M in) across full 1000K window
Prompt Cache Policy & TTL$0.1/1M cached read (90% off); 5-min default & 1-hour extended TTLAutomatic prefix caching at $0.1/1M (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)
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
Output Modalitiestexttext

Claude Haiku 5.5 vs GPT-6.1 Sol: Respuestas Rápidas (FAQ)

Which is more intelligent, Claude Haiku 5.5 or GPT-6.1 Sol?

GPT-6.1 Sol is more intelligent. It scores 94 compared with 62 on the composite Intelligence Index.

Which model is faster?

Claude Haiku 5.5 is faster, generating 160 tokens per second compared with 120 tokens per second.

Which model is cheaper to run?

Claude Haiku 5.5 is cheaper at $0.77 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Claude Haiku 5.5 has lower latency, with a time to first token of 0.22s compared with 1.12s.

Which model has a larger context window?

GPT-6.1 Sol has a larger context window, supporting 1M tokens compared with 200K tokens.

Veredicto

Choose Claude Haiku 5.5 for en tiempo real user-facing chatbots, rápido texto clasificación, code completion, y perimetral agente enrutamiento, or choose GPT-6.1 Sol for el mejor valor modelo de frontera para profesional y agentic trabajo.

Preguntas frecuentes

Which is cheaper: Claude Haiku 5.5 or GPT-6.1 Sol?+

Claude Haiku 5.5 costs $1/1M input and $5/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.

Which model has a larger context window: Claude Haiku 5.5 or GPT-6.1 Sol?+

Claude Haiku 5.5 supports 200K tokens whereas GPT-6.1 Sol supports 1M 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