Pixtral Large vs Aya Expanse 32B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Pixtral Large by Mistral AI and Aya Expanse 32B by Cohere 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, Aya Expanse 32B holds a notable pricing advantage for token ingestion. On output generation, Aya Expanse 32B is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 128K tokens. In latency-sensitive production environments, Aya Expanse 32B 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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Pixtral Large
Modelo de Pesos AbertosO Pixtral Large da Mistral AI é um modelo de inteligência artificial com janela de contexto de 128K tokens e preço de API de $2/1M na entrada e $6/1M na saída, projetado para programação, raciocínio e automação empresarial.
$2.00 / 1M
$6.00 / 1M
128K tokens
38.5%
Aya Expanse 32B
Modelo de Pesos AbertosO Aya Expanse 32B da Cohere é um modelo de inteligência artificial com janela de contexto de 128K tokens e preço de API de $0.25/1M na entrada e $0.5/1M na saída, projetado para programação, raciocínio e automação empresarial.
$0.25 / 1M
$0.50 / 1M
128K tokens
33.5%
Principais Diferenças em Resumo
- Aya Expanse 32B is 10.8x cheaper for API usage (input + output tokens combined).
- Aya Expanse 32B is faster at 85 tok/s vs 70 tok/s.
- Pixtral Large leads on SWE-bench coding (38.5% vs 33.5%).
| Feature | Pixtral Large | Aya Expanse 32B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 48 | 44 |
Provider | Mistral AI | Cohere |
Input Price Cost per million input tokens | $2.00 / 1M | $0.25 / 1M |
Output Price Cost per million output tokens | $6.00 / 1M | $0.50 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.70 / 1M | $0.14 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 8K | 8K |
Throughput Speed Tokens generated per second | 70 tok/s | 85 tok/s |
SWE-bench Verified Real-world software engineering | 38.5% | 33.5% |
GPQA Diamond Graduate-level reasoning | 55.4% | 50% |
Cached Input Price Prompt caching discount per 1M tokens | $1.00 / 1M | $0.06 / 1M |
Quem vence em cada categoria?
At-a-glance decision frameworkPontuações de Benchmarks Verificadas
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.
Latência e Tempo de Resposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interativo de Custo de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Pixtral Large · Strengths & Key Advantages
- Arquitetura Frontier Vision-Language Transformer avançada da Mistral AI com contexto de 128K tokens
- Excelente relação custo-benefício ($2/1M entrada, $6/1M saída) e baixa latência
- Forte desempenho em benchmarks de código, análise lógica e tarefas multilíngues
Considerations & Trade-offs
- Cargas de trabalho com contexto máximo exigem monitoramento de custos de tokens
- Requer engenharia de prompt adequada para fluxos de trabalho altamente específicos
Aya Expanse 32B · Strengths & Key Advantages
- Arquitetura High-Capacity Multilingual Transformer avançada da Cohere com contexto de 128K tokens
- Excelente relação custo-benefício ($0.25/1M entrada, $0.5/1M saída) e baixa latência
- Forte desempenho em benchmarks de código, análise lógica e tarefas multilíngues
Considerations & Trade-offs
- Cargas de trabalho com contexto máximo exigem monitoramento de custos de tokens
- Requer engenharia de prompt adequada para fluxos de trabalho altamente específicos
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Pixtral Large | Aya Expanse 32B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.65 | $0.18 |
Output Tokens / Task Tokens generated per task | 35K | 28K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | Pixtral Large | Aya Expanse 32B |
|---|---|---|
| Official API Model ID (SDK) | pixtral-large-latest | aya-expanse-32b |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-11 | 2024-10 |
| Knowledge Cutoff | October 2024 | August 2024 |
| Architecture | Frontier Vision-Language Transformer | High-Capacity Multilingual Transformer |
| Parameters | 124B | 32B |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($2/1M in) across full 128K window | Flat rate ($0.25/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Cached input at $1/1M tokens | Cached input at $0.06/1M tokens |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| 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) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 20 GB VRAM (4-bit Quantized) · 69 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text |
| Output Modalities | text | text |
Pixtral Large vs Aya Expanse 32B: Respostas Rápidas (FAQ)
Which is more intelligent, Pixtral Large or Aya Expanse 32B?
Pixtral Large is more intelligent. It scores 48 compared with 44 on the composite Intelligence Index.
Which model is faster?
Aya Expanse 32B is faster, generating 85 tokens per second compared with 70 tokens per second.
Which model is cheaper to run?
Aya Expanse 32B is cheaper at $0.14 per 1M tokens blended, compared with $1.70 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Aya Expanse 32B has lower latency, with a time to first token of 0.44s compared with 0.55s.
Veredito
Verdict & Model Selection Recommendation: Choose Pixtral Large if your priority is Enterprise document understanding, financial visual audit, and sovereign multimodal automation. Its performance in demanding tasks justifies its operational token cost. Choose Aya Expanse 32B if your workflow prioritizes Global enterprise communications, international legal translation, and academic research. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Perguntas Frequentes (FAQ)
How does Pixtral Large compare to Aya Expanse 32B in API token pricing and cost per token?+
Pixtral Large costs $2.00 per 1M input tokens and $6.00 per 1M output tokens, whereas Aya Expanse 32B costs $0.25 input and $0.50 output. Overall, Aya Expanse 32B 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: Pixtral Large or Aya Expanse 32B?+
Both Pixtral Large and Aya Expanse 32B offer an identical context window of 128K tokens (~96k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Pixtral Large or Aya Expanse 32B?+
Pixtral Large scores 38.5% on SWE-bench Verified while Aya Expanse 32B scores 33.5%. For autonomous software engineering and multi-file debugging, Pixtral Large holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Pixtral Large and Aya Expanse 32B?+
Pixtral Large is optimized for low-latency direct generation. In contrast, Aya Expanse 32B focuses on standard fast inference.
How does inference speed (tokens per second) compare between Pixtral Large and Aya Expanse 32B?+
Pixtral Large generates approximately 70 tokens per second, while Aya Expanse 32B delivers 85 tokens per second. In latency-sensitive production environments, Aya Expanse 32B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Pixtral Large and Aya Expanse 32B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Aya Expanse 32B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Pixtral Large. 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.
Fontes oficiais: GetInfoUs Independent AI Benchmarks & Official API Specs