Aya Expanse 8B vs Qwen 2.5 7B Instruct
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Aya Expanse 8B da Cohere e Qwen 2.5 7B Instruct da Alibaba são dois modelos de IA de destaque. O Aya Expanse 8B oferece uma janela de contexto de 8.2K tokens a $0.08/$0.16 por milhão de tokens, enquanto o Qwen 2.5 7B Instruct oferece 131.1K tokens de contexto a $0.06/$0.12 por milhão de tokens. Veja abaixo a comparação completa de benchmarks, latência e custos.
Troque e Compare Qualquer Modelo
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Aya Expanse 8B
Modelo Multilíngue GlobalO Aya Expanse 8B da Cohere é um modelo de IA projetado para tradução multilíngue precisa, análise transcultural e fluxos globais, oferecendo janela de contexto de 8K, velocidade de 135 tokens/s, índice de inteligência de 36/100 e tarifa de
$0.08 / 1M
$0.16 / 1M
8.2K tokens
24%
Qwen 2.5 7B Instruct
Modelo Leve e EconômicoO Qwen 2.5 7B Instruct da Alibaba é um modelo de IA projetado para máxima economia por token e alto desempenho em escala de produção, oferecendo janela de contexto de 131K, velocidade de 130 tokens/s, índice de inteligência de 35/100 e tari
$0.06 / 1M
$0.12 / 1M
131.1K tokens
28.5%
Principais Diferenças em Resumo
- Qwen 2.5 7B Instruct is 1.4x cheaper for API usage (input + output tokens combined).
- Qwen 2.5 7B Instruct has a larger context window (131K vs 8K tokens).
- Aya Expanse 8B is faster at 135 tok/s vs 130 tok/s.
- Qwen 2.5 7B Instruct leads on SWE-bench coding (28.5% vs 24.0%).
| Feature | Aya Expanse 8B | Qwen 2.5 7B Instruct |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 36 | 35 |
Provider | Cohere | Alibaba |
Input Price Cost per million input tokens | $0.08 / 1M | $0.06 / 1M |
Output Price Cost per million output tokens | $0.16 / 1M | $0.12 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.05 / 1M | $0.03 / 1M |
Context Window Maximum input + output tokens | 8.2K tokens~12 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 4K | 8K |
Throughput Speed Tokens generated per second | 135 tok/s | 130 tok/s |
SWE-bench Verified Real-world software engineering | 24% | 28.5% |
GPQA Diamond Graduate-level reasoning | 40% | 36.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.02 / 1M | $0.01 / 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.
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.
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.
Aya Expanse 8B · Strengths & Key Advantages
- Janela de contexto de 8K com limite máximo de saída de 4K tokens (SWE-bench Verified 24% · GPQA Diamond 40%)
- Taxa de transferência rápida de 135 tokens/segundo com latência inicial (TTFT) de 300 ms
- Pesos abertos (8B) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama
Considerations & Trade-offs
- Requer servidores GPU com memória VRAM adequada para execução local em precisão total
- Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade
Qwen 2.5 7B Instruct · Strengths & Key Advantages
- Janela de contexto de 131K com limite máximo de saída de 8K tokens (SWE-bench Verified 28.5% · GPQA Diamond 36.8%)
- Taxa de transferência rápida de 130 tokens/segundo com latência inicial (TTFT) de 290 ms
- Pesos abertos (7B) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama
Considerations & Trade-offs
- Requer servidores GPU com memória VRAM adequada para execução local em precisão total
- Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Aya Expanse 8B | Qwen 2.5 7B Instruct |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.04 | $0.05 |
Output Tokens / Task Tokens generated per task | 18K | 20K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | Aya Expanse 8B | Qwen 2.5 7B Instruct |
|---|---|---|
| Official API Model ID (SDK) | aya-expanse-8b | qwen-2-5-7b-instruct |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-10 | 2024-09 |
| Knowledge Cutoff | August 2024 | June 2024 |
| Architecture | Compact Multilingual Dense Transformer | Dense Autoregressive Transformer |
| Parameters | 8B | 7B |
| 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 ($0.08/1M in) across full 8K window | Flat rate ($0.06/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Cached input at $0.02/1M tokens | Cached input at $0.012/1M 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) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 5 GB VRAM (4-bit Quantized) · 17 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 4 GB VRAM (4-bit Quantized) · 15 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 | text |
| Output Modalities | text | text |
Aya Expanse 8B vs Qwen 2.5 7B Instruct: Respostas Rápidas (FAQ)
Which is more intelligent, Aya Expanse 8B or Qwen 2.5 7B Instruct?
Aya Expanse 8B is more intelligent. It scores 36 compared with 35 on the composite Intelligence Index.
Which model is faster?
Aya Expanse 8B is faster, generating 135 tokens per second compared with 130 tokens per second.
Which model is cheaper to run?
Qwen 2.5 7B Instruct is cheaper at $0.03 per 1M tokens blended, compared with $0.05 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Qwen 2.5 7B Instruct has lower latency, with a time to first token of 0.29s compared with 0.30s.
Which model has a larger context window?
Qwen 2.5 7B Instruct has a larger context window, supporting 131.1K tokens compared with 8.2K tokens.
Veredito
Tanto Aya Expanse 8B (Indicado para tradução multilíngue precisa, análise transcultural e fluxos globais, arquiteturas RAG corporativas e integração escalável via API.) quanto Qwen 2.5 7B Instruct (Indicado para máxima economia por token e alto desempenho em escala de produção, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: Aya Expanse 8B lidera em inteligência geral e programação, Aya Expanse 8B oferece geração mais rápida e Qwen 2.5 7B Instruct apresenta o melhor custo-benefício em produção.
Perguntas Frequentes (FAQ)
Qual é melhor para programação e raciocínio, Aya Expanse 8B ou Qwen 2.5 7B Instruct?+
Nas avaliações de inteligência e código, Aya Expanse 8B leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.
Qual modelo tem a API mais barata, Aya Expanse 8B ou Qwen 2.5 7B Instruct?+
O Aya Expanse 8B custa $0.08 / $0.16 por 1M de tokens (entrada/saída) e o Qwen 2.5 7B Instruct custa $0.06 / $0.12. No custo combinado, Qwen 2.5 7B Instruct é mais econômico.
Qual modelo possui maior janela de contexto e velocidade?+
O Aya Expanse 8B suporta 8.2K tokens de contexto contra 131.1K tokens do Qwen 2.5 7B Instruct, enquanto Aya Expanse 8B entrega maior velocidade de geração.
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: Documentação oficial de API da Cohere e Alibaba e benchmarks independentes (2026).