GLM-4.5 vs Qwen3-Next 80B-A3B (Thinking)
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GLM-4.5 da Zhipu AI e Qwen3-Next 80B-A3B (Thinking) da Alibaba são dois modelos de IA de destaque. O GLM-4.5 oferece uma janela de contexto de 131.1K tokens a $0.00/$0.00 por milhão de tokens, enquanto o Qwen3-Next 80B-A3B (Thinking) oferece 131.1K tokens de contexto a $0.00/$0.00 por milhão de tokens. Veja abaixo a comparação completa de benchmarks, latência e custos.
Troque e Compare Qualquer Modelo
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GLM-4.5
Raciocínio de Pesos AbertosO GLM-4.5 da Zhipu AI é um modelo de IA projetado para raciocínio em cadeia de pensamento com pesos abertos e alta eficiência, oferecendo janela de contexto de 131K, velocidade de 75 tokens/s, índice de inteligência de 80/100 e tarifa de ac
$0.00 / 1M
$0.00 / 1M
131.1K tokens
42%
Qwen3-Next 80B-A3B (Thinking)
Modelo Base de Pesos AbertosO Qwen3-Next 80B-A3B (Thinking) da Alibaba é um modelo de IA projetado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, oferecendo janela de contexto de 131K, velocidade de 95 tokens/s, índice de inteligênci
$0.00 / 1M
$0.00 / 1M
131.1K tokens
46%
Principais Diferenças em Resumo
- Both models cost the same under this workload — choose on capability, not price.
- Qwen3-Next 80B-A3B (Thinking) is faster at 95 tok/s vs 75 tok/s.
- Qwen3-Next 80B-A3B (Thinking) leads on SWE-bench coding (46.0% vs 42.0%).
| Feature | GLM-4.5 | Qwen3-Next 80B-A3B (Thinking) |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 80 | 82 |
Provider | Zhipu AI | Alibaba |
Input Price Cost per million input tokens | $0.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 131.1K tokens~197 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 8K | 16K |
Throughput Speed Tokens generated per second | 75 tok/s | 95 tok/s |
SWE-bench Verified Real-world software engineering | 42% | 46% |
GPQA Diamond Graduate-level reasoning | 61% | 68.5% |
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.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
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.
GLM-4.5 · Strengths & Key Advantages
- Janela de contexto de 131K com limite máximo de saída de 8K tokens (SWE-bench Verified 42% · GPQA Diamond 61%)
- Taxa de transferência rápida de 75 tokens/segundo com latência inicial (TTFT) de 550 ms
- Pesos abertos (130B MoE) 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
Qwen3-Next 80B-A3B (Thinking) · Strengths & Key Advantages
- Janela de contexto de 131K com limite máximo de saída de 16K tokens (SWE-bench Verified 46% · GPQA Diamond 68.5%)
- Taxa de transferência rápida de 95 tokens/segundo com latência inicial (TTFT) de 540 ms
- Pesos abertos (80B total (3B MoE)) 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 | GLM-4.5 | Qwen3-Next 80B-A3B (Thinking) |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 16K | 20K |
Reasoning Tokens / Task Thinking tokens burned per task | 6K | 12K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | GLM-4.5 | Qwen3-Next 80B-A3B (Thinking) |
|---|---|---|
| Official API Model ID (SDK) | glm-4-5 | qwen3-next-80b-a3b-thinking-instruct |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-07 | 2025-09 |
| Knowledge Cutoff | May 2025 | August 2025 |
| Architecture | Hybrid Reasoning Mixture-of-Experts Transformer | Deliberate Reasoning Sparse Mixture-of-Experts |
| Parameters | 130B MoE | 80B total (3B active MoE) |
| 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) | Free across full context window (only KV-cache VRAM scales) | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| 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) · 81 GB VRAM (4-bit Quantized) · 280 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 50 GB VRAM (4-bit Quantized) · 172 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 |
GLM-4.5 vs Qwen3-Next 80B-A3B (Thinking): Respostas Rápidas (FAQ)
Which is more intelligent, GLM-4.5 or Qwen3-Next 80B-A3B (Thinking)?
Qwen3-Next 80B-A3B (Thinking) is more intelligent. It scores 82 compared with 80 on the composite Intelligence Index.
Which model is faster?
Qwen3-Next 80B-A3B (Thinking) is faster, generating 95 tokens per second compared with 75 tokens per second.
Which model has lower latency?
Qwen3-Next 80B-A3B (Thinking) has lower latency, with a time to first token of 0.54s compared with 0.55s.
Veredito
Tanto GLM-4.5 (Indicado para raciocínio em cadeia de pensamento com pesos abertos e alta eficiência, arquiteturas RAG corporativas e integração escalável via API.) quanto Qwen3-Next 80B-A3B (Thinking) (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: Qwen3-Next 80B-A3B (Thinking) lidera em inteligência geral e programação, Qwen3-Next 80B-A3B (Thinking) oferece geração mais rápida e GLM-4.5 apresenta o melhor custo-benefício em produção.
Perguntas Frequentes (FAQ)
Qual é melhor para programação e raciocínio, GLM-4.5 ou Qwen3-Next 80B-A3B (Thinking)?+
Nas avaliações de inteligência e código, Qwen3-Next 80B-A3B (Thinking) leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.
Qual modelo tem a API mais barata, GLM-4.5 ou Qwen3-Next 80B-A3B (Thinking)?+
O GLM-4.5 custa $0.00 / $0.00 por 1M de tokens (entrada/saída) e o Qwen3-Next 80B-A3B (Thinking) custa $0.00 / $0.00. No custo combinado, GLM-4.5 é mais econômico.
Qual modelo possui maior janela de contexto e velocidade?+
O GLM-4.5 suporta 131.1K tokens de contexto contra 131.1K tokens do Qwen3-Next 80B-A3B (Thinking), enquanto Qwen3-Next 80B-A3B (Thinking) 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 Zhipu AI e Alibaba e benchmarks independentes (2026).