Mistral Small 4 vs Claude Haiku 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Mistral Small 4 di Mistral AI e Claude Haiku 5.5 di Anthropic sono due modelli di IA di riferimento. Mistral Small 4 offre una finestra di contesto di 256K token a $0.10/$0.30 per milione di token, mentre Claude Haiku 5.5 mette a disposizione 200K token di contesto a $1.00/$5.00 per milione di token. Di seguito analizziamo benchmark, latenza e costi.
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Mistral Small 4
Inferenza Ultra-RapidaMistral Small 4 di Mistral AI è un modello di IA progettato per le interazioni in tempo reale a bassissima latenza e il routing ad alto volume, con finestra di contesto da 256K, velocità di 120 token/s, indice di intelligenza di 48/100 e ta
$0.10 / 1M
$0.30 / 1M
256K tokens
44.5%
Claude Haiku 5.5
Inferenza Ultra-RapidaClaude Haiku 5.5 di Anthropic è un modello di IA progettato per le interazioni in tempo reale a bassissima latenza e il routing ad alto volume, con finestra di contesto da 200K, velocità di 160 token/s, indice di intelligenza di 62/100 e ta
$1.00 / 1M
$5.00 / 1M
200K tokens
68.4%
Differenze Chiave a Colpo d’Occhio
- Mistral Small 4 is 13.2x cheaper for API usage (input + output tokens combined).
- Mistral Small 4 has a larger context window (256K vs 200K tokens).
- Claude Haiku 5.5 is faster at 160 tok/s vs 120 tok/s.
- Claude Haiku 5.5 leads on SWE-bench coding (68.4% vs 44.5%).
| Feature | Mistral Small 4 | Claude Haiku 5.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 48 | 62 |
Provider | Mistral AI | Anthropic |
Input Price Cost per million input tokens | $0.10 / 1M | $1.00 / 1M |
Output Price Cost per million output tokens | $0.30 / 1M | $5.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.07 / 1M | $0.77 / 1M |
Context Window Maximum input + output tokens | 256K tokens~384 A4 pages of text | 200K tokens~300 A4 pages of text |
Max Output Tokens Per single request | 16K | 64K |
Throughput Speed Tokens generated per second | 120 tok/s | 160 tok/s |
SWE-bench Verified Real-world software engineering | 44.5% | 68.4% |
GPQA Diamond Graduate-level reasoning | 56% | 69.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 1M | $0.10 / 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.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
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.
Mistral Small 4 · Strengths & Key Advantages
- Finestra di contesto da 256K con capacità massima di output fino a 16K token (SWE-bench Verified 44.5% · GPQA Diamond 56%)
- Throughput elevato di 120 token/secondo con latenza al primo token (TTFT) di soli 340 ms
- Pesi aperti (24B) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
- Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
Claude Haiku 5.5 · Strengths & Key Advantages
- Finestra di contesto da 200K con capacità massima di output fino a 64K token (SWE-bench Verified 68.4% · GPQA Diamond 69.8%)
- Throughput elevato di 160 token/secondo con latenza al primo token (TTFT) di soli 220 ms
- Economia API competitiva ($1/$5 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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Mistral Small 4 | Claude Haiku 5.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.12 | $0.70 |
Output Tokens / Task Tokens generated per task | 28K | 20K |
Reasoning Tokens / Task Thinking tokens burned per task | 12K | 10K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | Mistral Small 4 | Claude Haiku 5.5 |
|---|---|---|
| Official API Model ID (SDK) | mistral-small-4-latest | claude-haiku-5-5-20261001 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2026-03 | 2026-10 |
| Knowledge Cutoff | January 2026 | August 2026 |
| Architecture | Next-Gen Compact Multimodal Transformer | Ultra-Fast Lightweight Reasoning Transformer |
| Parameters | 24B | 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) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.1/1M in) across full 256K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.03/1M tokens | $0.1/1M 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) · 15 GB VRAM (4-bit Quantized) · 52 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 |
Mistral Small 4 vs Claude Haiku 5.5: Risposte Rapide (FAQ)
Which is more intelligent, Mistral Small 4 or Claude Haiku 5.5?
Claude Haiku 5.5 is more intelligent. It scores 62 compared with 48 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?
Mistral Small 4 is cheaper at $0.07 per 1M tokens blended, compared with $0.77 (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 0.34s.
Which model has a larger context window?
Mistral Small 4 has a larger context window, supporting 256K tokens compared with 200K tokens.
Verdetto
Sia Mistral Small 4 (Ideale per le interazioni in tempo reale a bassissima latenza e il routing ad alto volume, sistemi RAG aziendali e integrazioni API scalabili.) sia Claude Haiku 5.5 (Ideale per le interazioni in tempo reale a bassissima latenza e il routing ad alto volume, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Claude Haiku 5.5 guida nei benchmark di ragionamento e programmazione, Claude Haiku 5.5 garantisce una generazione più rapida e Mistral Small 4 offre il miglior rapporto qualità-prezzo per carichi API elevati.
Domande Frequenti (FAQ)
Quale modello è migliore per coding e ragionamento, Mistral Small 4 o Claude Haiku 5.5?+
Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Claude Haiku 5.5. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.
Qual è la differenza di prezzo API tra Mistral Small 4 e Claude Haiku 5.5?+
Mistral Small 4 costa $0.10 / $0.30 per milione di token di input/output, mentre Claude Haiku 5.5 costa $1.00 / $5.00. Nel costo combinato, Mistral Small 4 risulta più economico.
Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+
Mistral Small 4 supporta 256K token di contesto contro 200K token di Claude Haiku 5.5, mentre Claude Haiku 5.5 offre una velocità di generazione superiore.
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: Documentazione API ufficiale di Mistral AI e Anthropic, schede di sistema e benchmark indipendenti (2026).