GPT-4.1 nano vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT-4.1 nano by OpenAI against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.
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GPT-4.1 nano
Modello Leggero ed EconomicoGPT-4.1 nano di OpenAI è un modello di IA progettato per la massima efficienza dei costi per token e l’inferenza rapida in produzione, con finestra di contesto da 1.0M, velocità di 220 token/s, indice di intelligenza di 26/100 e tariffa di
$0.10 / 1M
$0.40 / 1M
1.05M tokens
24%
Claude Opus 5.5
Modello Ammiraglia di FrontieraClaude Opus 5.5 di Anthropic è un modello di IA progettato per il ragionamento aziendale di frontiera, la programmazione avanzata e i carichi di produzione, con finestra di contesto da 1M, velocità di 78 token/s, indice di intelligenza di 9
$4.00 / 1M
$20.00 / 1M
1M tokens
84.6%
Differenze Chiave a Colpo d’Occhio
- GPT-4.1 nano is 44.9x cheaper for API usage (input + output tokens combined).
- GPT-4.1 nano has a larger context window (1M vs 1M tokens).
- GPT-4.1 nano is faster at 220 tok/s vs 78 tok/s.
- Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 24.0%).
| Feature | GPT-4.1 nano | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 26 | 94 |
Provider | OpenAI | Anthropic |
Input Price Cost per million input tokens | $0.10 / 1M | $4.00 / 1M |
Output Price Cost per million output tokens | $0.40 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.08 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,571 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 33K | 128K |
Throughput Speed Tokens generated per second | 220 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | 24% | 84.6% |
GPQA Diamond Graduate-level reasoning | 38% | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 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-4.1 nano · Strengths & Key Advantages
- Finestra di contesto da 1.0M con capacità massima di output fino a 33K token (SWE-bench Verified 24% · GPQA Diamond 38%)
- Throughput elevato di 220 token/secondo con latenza al primo token (TTFT) di soli 220 ms
- Economia API competitiva ($0.1/$0.4 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati
Considerations & Trade-offs
- Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
Claude Opus 5.5 · Strengths & Key Advantages
- Finestra di contesto da 1M con capacità massima di output fino a 128K token (SWE-bench Verified 84.6% · GPQA Diamond 82.3%)
- Throughput elevato di 78 token/secondo con latenza al primo token (TTFT) di soli 1400 ms
- Economia API competitiva ($4/$20 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati
Considerations & Trade-offs
- Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
- Per volumi di traffico elevati in produzione si consiglia di attivare il prompt caching e la Batch API
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-4.1 nano | Claude Opus 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.04 | $3.85 |
Output Tokens / Task Tokens generated per task | 42K | 68K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 42K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-4.1 nano | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | gpt-4-1-nano | claude-opus-5-5-20260901 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2025-04 | 2026-09 |
| Knowledge Cutoff | June 2024 | June 2026 |
| Architecture | Dense multimodal transformer (nano) | 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 ($0.1/1M in) across full 1048K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.025/1M (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-4.1 nano vs Claude Opus 5.5: Risposte Rapide (FAQ)
Which is more intelligent, GPT-4.1 nano or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 26 on the composite Intelligence Index.
Which model is faster?
GPT-4.1 nano is faster, generating 220 tokens per second compared with 78 tokens per second.
Which model is cheaper to run?
GPT-4.1 nano is cheaper at $0.08 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-4.1 nano has lower latency, with a time to first token of 0.22s compared with 1.40s.
Which model has a larger context window?
GPT-4.1 nano has a larger context window, supporting 1.05M tokens compared with 1M tokens.
Verdetto
Choose GPT-4.1 nano for ideale per la massima efficienza dei costi per token e l’inferenza rapida in produzione, sistemi rag aziendali e integrazioni api scalabili., or choose Claude Opus 5.5 for ideale per il ragionamento aziendale di frontiera, la programmazione avanzata e i carichi di produzione, sistemi rag aziendali e integrazioni api scalabili..
Domande Frequenti (FAQ)
Which is cheaper: GPT-4.1 nano or Claude Opus 5.5?+
GPT-4.1 nano costs $0.1/1M input and $0.4/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.
Which model has a larger context window: GPT-4.1 nano or Claude Opus 5.5?+
GPT-4.1 nano supports 1.0M tokens whereas Claude Opus 5.5 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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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: OpenAI & Anthropic Official Documentation