GPT-5.6 Sol vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GPT-5.6 Sol by OpenAI and Claude Opus 5.5 by Anthropic 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, GPT-5.6 Sol provides an expanded memory horizon with 1,050K tokens. In latency-sensitive production environments, GPT-5.6 Sol 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-5.6 Sol
Modello di Ragionamento ProfondoGPT-5.6 Sol di OpenAI è un modello di intelligenza artificiale con finestra di contesto da 1.1M token e tariffazione API di $4/1M token in input e $20/1M in output, ideale per coding, ragionamento e automazione aziendale.
$4.00 / 1M
$20.00 / 1M
1.05M tokens
83.8%
Claude Opus 5.5
Modello di Ragionamento ProfondoClaude Opus 5.5 di Anthropic è un modello di intelligenza artificiale con finestra di contesto da 1M token e tariffazione API di $4.0/1M token in input e $20.0/1M in output, ideale per coding, ragionamento e automazione aziendale.
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
Differenze Chiave a Colpo d’Occhio
- Both models cost the same under this workload — choose on capability, not price.
- GPT-5.6 Sol has a larger context window (1M vs 1M tokens).
- GPT-5.6 Sol is faster at 108 tok/s vs 78 tok/s.
- Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 83.8%).
| Feature | GPT-5.6 Sol | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 89 | 94 |
Provider | OpenAI | Anthropic |
Input Price Cost per million input tokens | $4.00 / 1M | $4.00 / 1M |
Output Price Cost per million output tokens | $20.00 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $3.08 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,575 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 | 108 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | 83.8% | 84.6% |
GPQA Diamond Graduate-level reasoning | 82% | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $0.40 / 1M | $0.40 / 1M |
Chi vince in ogni categoria?
At-a-glance decision frameworkPunteggi Benchmark Verificati
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.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5.6 Sol · Strengths & Key Advantages
- Architettura Frontier unified reasoning model avanzata di OpenAI con supporto fino a 1.1M token di contesto
- Ottimo rapporto qualità-prezzo ($4/1M input, $20/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
Claude Opus 5.5 · Strengths & Key Advantages
- Architettura Extended Autonomous Hybrid Reasoning Transformer avanzata di Anthropic con supporto fino a 1M token di contesto
- Ottimo rapporto qualità-prezzo ($4.0/1M input, $20.0/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-5.6 Sol | Claude Opus 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $3.30 | $3.85 |
Output Tokens / Task Tokens generated per task | 64K | 68K |
Reasoning Tokens / Task Thinking tokens burned per task | 38K | 42K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-5.6 Sol | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-6-sol | claude-opus-5-5-20260901 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2026-07 | 2026-09 |
| Knowledge Cutoff | February 2026 | June 2026 |
| Architecture | Frontier unified reasoning model | Extended Autonomous Hybrid Reasoning Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($4/1M in) across full 1050K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.4/1M (no extra cache-write fee) | $0.4/1M cached read (90% off); 5-min default & 1-hour extended TTL |
| 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 Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Custom Model Tuning via Amazon Bedrock & Prompt Distillation |
| 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 | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated |
| 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-5.6 Sol vs Claude Opus 5.5: Risposte Rapide (FAQ)
Which is more intelligent, GPT-5.6 Sol or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 89 on the composite Intelligence Index.
Which model is faster?
GPT-5.6 Sol is faster, generating 108 tokens per second compared with 78 tokens per second.
Which model has lower latency?
GPT-5.6 Sol has lower latency, with a time to first token of 1.21s compared with 1.40s.
Which model has a larger context window?
GPT-5.6 Sol has a larger context window, supporting 1.05M tokens compared with 1M tokens.
Verdetto
Verdict & Model Selection Recommendation: Choose GPT-5.6 Sol if your priority is Complex professional work and agentic coding pipelines. Its performance in demanding tasks justifies its operational token cost. Choose Claude Opus 5.5 if your workflow prioritizes Large-scale software development agents, enterprise codebases, and mission-critical scientific synthesis. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Domande Frequenti (FAQ)
How does GPT-5.6 Sol compare to Claude Opus 5.5 in API token pricing and cost per token?+
Both GPT-5.6 Sol and Claude Opus 5.5 share matched API token pricing at $4.00 per 1M input tokens and $20.00 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-5.6 Sol or Claude Opus 5.5?+
GPT-5.6 Sol supports a context window of 1,050K tokens (~788k words), while Claude Opus 5.5 supports 1,000K tokens (~750k words). Therefore, GPT-5.6 Sol can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GPT-5.6 Sol or Claude Opus 5.5?+
GPT-5.6 Sol scores 83.8% on SWE-bench Verified while Claude Opus 5.5 scores 84.6%. For autonomous software engineering and multi-file debugging, Claude Opus 5.5 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GPT-5.6 Sol and Claude Opus 5.5?+
GPT-5.6 Sol features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Claude Opus 5.5 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GPT-5.6 Sol and Claude Opus 5.5?+
GPT-5.6 Sol generates approximately 108 tokens per second, while Claude Opus 5.5 delivers 78 tokens per second. In latency-sensitive production environments, GPT-5.6 Sol provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-5.6 Sol and Claude Opus 5.5 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-5.6 Sol, and route deep architectural or complex reasoning queries to GPT-5.6 Sol. 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.
Fonti ufficiali: GetInfoUs Independent AI Benchmarks & Official API Specs