Claude Sonnet 4.6 vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Claude Sonnet 4.6 by Anthropic against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
Changez et Comparez les Modèles
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Live comparison: no in-depth editorial review has been published for this pair yet.
Claude Sonnet 4.6
Bourreau de productionle daily driver d'entreprise champion d'early 2026, pairing un 1-millions-token fenêtre de contexte avec rapide débit et rock-solid appel d'outils.
$3.00 / 1M
$15.00 / 1M
1M tokens
76.1%
GPT-6.1 Sol
Valeur proche d'Astraoffre results close à GPT-6 Astra à roughly one fifth du prix — le latest 2026 version.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Différences Clés en un Coup d’Œil
- GPT-6.1 Sol is 1.5x cheaper for API usage (input + output tokens combined).
- GPT-6.1 Sol is faster at 120 tok/s vs 92 tok/s.
- GPT-6.1 Sol leads on SWE-bench coding (82.3% vs 76.1%).
| Caractéristique | Claude Sonnet 4.6 | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 60 | 94 |
Provider | Anthropic | OpenAI |
Input Price Cost per million input tokens | $3.00 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $15.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $2.31 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 128K | 128K |
Throughput Speed Tokens generated per second | 92 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | 76.1% | 82.3% |
GPQA Diamond Graduate-level reasoning | 74% | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.30 / 1M | $0.10 / 1M |
Qui gagne où ?
At-a-glance decision frameworkScores de Benchmarks Vérifiés
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.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Claude Sonnet 4.6 · Strengths & Key Advantages
- équilibré $3/$15 economics combined avec un complet 1M token fenêtre de contexte
- exceptionnel 92 tokens/sec débit pour interactif développeur environments
- Precise structuré JSON sortie conformité et utilisation de l'ordinateur agent exécution
Considerations & Trade-offs
- Slightly behind Opus 4.6 sur highly abstract mathématique recherche tâches
- nécessite careful prompt caching configuration à optimize 1M token ingestion coûts
GPT-6.1 Sol · Strengths & Key Advantages
- Near-Astra results à roughly un fifth du coût
- Excellent codage et à long terme agent performances
- solide reliability avec far inférieur latence que Astra
- Near-phare results à roughly un fifth du prix
Considerations & Trade-offs
- Still marginally behind Astra sur le très hardest raisonnement
- Same prix as GPT-6 Sol, so le upgrade decision est easy
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Claude Sonnet 4.6 | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $3.29 | $2.05 |
Output Tokens / Task Tokens generated per task | 72K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 37K | 36K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Claude Sonnet 4.6 | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | claude-sonnet-4-6-20260201 | gpt-6-1-sol |
| API Protocol | Anthropic Messages API + AWS Bedrock + Vertex AI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-02 | 2026-09 |
| Knowledge Cutoff | December 2025 | April 2026 |
| Architecture | High-Throughput Multimodal Agent Transformer | Near-Astra unified reasoning model |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM | 10,000 – 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 tokens | Flat rate ($2/1M in) across full 1000K window |
| Prompt Cache Policy & TTL | $0.3/1M cached read (90% off); 5-min default & 1-hour extended TTL | Automatic prefix caching at $0.1/1M (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 Citations + Code Execution Tool + MCP Connectors | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Custom Model Tuning via Amazon Bedrock & Prompt 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 | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated | 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 |
Claude Sonnet 4.6 vs GPT-6.1 Sol: Réponses Rapides (FAQ)
Which is more intelligent, Claude Sonnet 4.6 or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 60 on the composite Intelligence Index.
Which model is faster?
GPT-6.1 Sol is faster, generating 120 tokens per second compared with 92 tokens per second.
Which model is cheaper to run?
GPT-6.1 Sol is cheaper at $1.47 per 1M tokens blended, compared with $2.31 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Claude Sonnet 4.6 has lower latency, with a time to first token of 0.65s compared with 1.12s.
Verdict
Choose Claude Sonnet 4.6 for production codage copilots, complet-repo code recherche, client agent flux de travail, et automatisé flux de travail, or choose GPT-6.1 Sol for le meilleur valeur modèle de pointe pour professionnel et agentic travail.
Questions fréquentes
Which is cheaper: Claude Sonnet 4.6 or GPT-6.1 Sol?+
Claude Sonnet 4.6 costs $3/1M input and $15/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Claude Sonnet 4.6 or GPT-6.1 Sol?+
Claude Sonnet 4.6 supports 1M tokens whereas GPT-6.1 Sol supports 1M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: Anthropic & OpenAI Official Documentation