Mistral Small 4 vs Claude Haiku 5.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Mistral Small 4 by Mistral AI and Claude Haiku 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, Mistral Small 4 holds a notable pricing advantage for token ingestion. On output generation, Mistral Small 4 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Mistral Small 4 provides an expanded memory horizon with 256K tokens. In latency-sensitive production environments, Claude Haiku 5.5 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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Mistral Small 4
Modello a Pesi ApertiMistral Small 4 di Mistral AI è un modello di intelligenza artificiale con finestra di contesto da 256K token e tariffazione API di $0.15/1M token in input e $0.6/1M in output, ideale per coding, ragionamento e automazione aziendale.
$0.10 / 1M
$0.30 / 1M
256K tokens
44.5%
Claude Haiku 5.5
Modello di Ragionamento ProfondoClaude Haiku 5.5 di Anthropic è un modello di intelligenza artificiale con finestra di contesto da 200K token e tariffazione API di $1/1M token in input e $5/1M in output, ideale per coding, ragionamento e automazione aziendale.
$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
- Architettura Next-Gen Compact Multimodal Transformer avanzata di Mistral AI con supporto fino a 256K token di contesto
- Ottimo rapporto qualità-prezzo ($0.15/1M input, $0.6/1M 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 Haiku 5.5 · Strengths & Key Advantages
- Architettura Ultra-Fast Lightweight Reasoning Transformer avanzata di Anthropic con supporto fino a 200K token di contesto
- Ottimo rapporto qualità-prezzo ($1/1M input, $5/1M 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 | 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
Verdict & Model Selection Recommendation: Choose Mistral Small 4 if your priority is High-volume agent orchestration, low-cost document parsing, and edge deployments. Its performance in demanding tasks justifies its operational token cost. Choose Claude Haiku 5.5 if your workflow prioritizes Real-time user-facing chatbots, rapid text classification, code completion, and edge agent routing. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Domande Frequenti (FAQ)
How does Mistral Small 4 compare to Claude Haiku 5.5 in API token pricing and cost per token?+
Mistral Small 4 costs $0.10 per 1M input tokens and $0.30 per 1M output tokens, whereas Claude Haiku 5.5 costs $1.00 input and $5.00 output. Overall, Mistral Small 4 is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Mistral Small 4 or Claude Haiku 5.5?+
Mistral Small 4 supports a context window of 256K tokens (~192k words), while Claude Haiku 5.5 supports 200K tokens (~150k words). Therefore, Mistral Small 4 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Mistral Small 4 or Claude Haiku 5.5?+
Mistral Small 4 scores 44.5% on SWE-bench Verified while Claude Haiku 5.5 scores 68.4%. For autonomous software engineering and multi-file debugging, Claude Haiku 5.5 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Mistral Small 4 and Claude Haiku 5.5?+
Mistral Small 4 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Claude Haiku 5.5 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Mistral Small 4 and Claude Haiku 5.5?+
Mistral Small 4 generates approximately 120 tokens per second, while Claude Haiku 5.5 delivers 160 tokens per second. In latency-sensitive production environments, Claude Haiku 5.5 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Mistral Small 4 and Claude Haiku 5.5 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Mistral Small 4 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Claude Haiku 5.5. This tiered routing pattern cuts overall API bills by up to 70%.
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