Pixtral Large vs Claude Opus 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Pixtral Large by Mistral AI against Claude Opus 5.5 by Anthropic across real-world coding, reasoning, token economics, and latency.
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Pixtral Large
Modello a Pesi ApertiPixtral Large di Mistral AI è un modello di intelligenza artificiale con finestra di contesto da 128K token e tariffazione API di $2/1M token in input e $6/1M in output, ideale per coding, ragionamento e automazione aziendale.
$2.00 / 1M
$6.00 / 1M
128K tokens
38.5%
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
- Pixtral Large is 2.5x cheaper for API usage (input + output tokens combined).
- Claude Opus 5.5 has a larger context window (1M vs 128K tokens).
- Claude Opus 5.5 is faster at 78 tok/s vs 70 tok/s.
- Claude Opus 5.5 leads on SWE-bench coding (84.6% vs 38.5%).
| Feature | Pixtral Large | Claude Opus 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 48 | 94 |
Provider | Mistral AI | Anthropic |
Input Price Cost per million input tokens | $2.00 / 1M | $4.00 / 1M |
Output Price Cost per million output tokens | $6.00 / 1M | $20.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.70 / 1M | $3.08 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 128K |
Throughput Speed Tokens generated per second | 70 tok/s | 78 tok/s |
SWE-bench Verified Real-world software engineering | 38.5% | 84.6% |
GPQA Diamond Graduate-level reasoning | 55.4% | 82.3% |
Cached Input Price Prompt caching discount per 1M tokens | $1.00 / 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.
Pixtral Large · Strengths & Key Advantages
- Architettura Frontier Vision-Language Transformer avanzata di Mistral AI con supporto fino a 128K token di contesto
- Ottimo rapporto qualità-prezzo ($2/1M input, $6/1M 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 | Pixtral Large | Claude Opus 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.65 | $3.85 |
Output Tokens / Task Tokens generated per task | 35K | 68K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 42K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | Pixtral Large | Claude Opus 5.5 |
|---|---|---|
| Official API Model ID (SDK) | pixtral-large-latest | claude-opus-5-5-20260901 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2024-11 | 2026-09 |
| Knowledge Cutoff | October 2024 | June 2026 |
| Architecture | Frontier Vision-Language Transformer | Extended Autonomous Hybrid Reasoning Transformer |
| Parameters | 124B | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($2/1M in) across full 128K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $1/1M tokens | $0.4/1M cached read (90% off); 5-min default & 1-hour extended TTL |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | 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 | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 77 GB VRAM (4-bit Quantized) · 267 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
Pixtral Large vs Claude Opus 5.5: Risposte Rapide (FAQ)
Which is more intelligent, Pixtral Large or Claude Opus 5.5?
Claude Opus 5.5 is more intelligent. It scores 94 compared with 48 on the composite Intelligence Index.
Which model is faster?
Claude Opus 5.5 is faster, generating 78 tokens per second compared with 70 tokens per second.
Which model is cheaper to run?
Pixtral Large is cheaper at $1.70 per 1M tokens blended, compared with $3.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Pixtral Large has lower latency, with a time to first token of 0.55s compared with 1.40s.
Which model has a larger context window?
Claude Opus 5.5 has a larger context window, supporting 1M tokens compared with 128K tokens.
Verdetto
Choose Pixtral Large for automazione enterprise, sviluppo software, agenti ia autonomi e pipeline di produzione mistral ai, or choose Claude Opus 5.5 for automazione enterprise, sviluppo software, agenti ia autonomi e pipeline di produzione anthropic.
Domande Frequenti (FAQ)
Which is cheaper: Pixtral Large or Claude Opus 5.5?+
Pixtral Large costs $2/1M input and $6/1M output tokens, while Claude Opus 5.5 costs $4/1M input and $20/1M output tokens.
Which model has a larger context window: Pixtral Large or Claude Opus 5.5?+
Pixtral Large supports 128K 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: Mistral AI & Anthropic Official Documentation