Head-to-Head Model Comparison

Hy3 vs Kimi K2.7 Code

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado pela equipe de pesquisa do GetInfoUsÚltima revisão: October 2026Política editorial
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Hy3 vs Kimi K2.7 Code: Benchmarks, Pricing & Speed Comparison

Hy3 da Tencent e Kimi K2.7 Code da Moonshot AI são dois modelos de IA de destaque. O Hy3 oferece uma janela de contexto de 262.1K tokens a $0.00/$0.00 por milhão de tokens, enquanto o Kimi K2.7 Code oferece 262.1K tokens de contexto a $0.00/$0.00 por milhão de tokens. Veja abaixo a comparação completa de benchmarks, latência e custos.

Comparação Direta (VS)

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Popular Matchups:
Tencent

Hy3

Raciocínio de Pesos Abertos

O Hy3 da Tencent é um modelo de IA projetado para raciocínio em cadeia de pensamento com pesos abertos e alta eficiência, oferecendo janela de contexto de 262K, velocidade de 85 tokens/s, índice de inteligência de 81/100 e tarifa de acesso

Intelligence Index81 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

N/A

Moonshot AI

Kimi K2.7 Code

Especialista Aberto em Código

O Kimi K2.7 Code da Moonshot AI é um modelo de IA projetado para desenvolvimento de software e geração de código com pesos abertos auto-hospedáveis, oferecendo janela de contexto de 262K, velocidade de 75 tokens/s, índice de inteligência de

Intelligence Index89 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

62%

Principais Diferenças em Resumo

  • Both models cost the same under this workload — choose on capability, not price.
  • Hy3 is faster at 85 tok/s vs 75 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureHy3Kimi K2.7 Code
Intelligence Index
Composite reasoning & knowledge score (0-100)
8189
Provider
TencentMoonshot AI
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
262.1K tokens~393 A4 pages of text262.1K tokens~393 A4 pages of text
Max Output Tokens
Per single request
16K32K
Throughput Speed
Tokens generated per second
85 tok/s75 tok/s
SWE-bench Verified
Real-world software engineering
N/A62%
GPQA Diamond
Graduate-level reasoning
64%73%

Quem vence em cada categoria?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Tied
Identical monthly runtime cost
Speed
Hy3
+13% faster generation
Context
Tied
262.1K tokens window

Pontuações de Benchmarks Verificadas

Official evaluation metrics verified by independent third-party research labs.

Hy3Kimi K2.7 Code
SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

N/Avs62%
GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

64%vs73%
MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

79%vs85%
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1330ptsvs1415pts
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs74%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

85 tok/s vs75 tok/s

Latência e Tempo de Resposta

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better
Hy30.46s
Kimi K2.7 Code0.48s
Est. End-to-End Response (500 tokens)TTFT + generation time
Hy36.34s
Kimi K2.7 Code7.15s

Simulador Interativo de Custo de Tokens

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Hy3 Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
Kimi K2.7 Code Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
These models cost the same under this workload — no savings either way.Break-even

Hy3 · Strengths & Key Advantages

  • Janela de contexto de 262K com limite máximo de saída de 16K tokens (GPQA Diamond 64% · MMLU-Pro 79%)
  • Taxa de transferência rápida de 85 tokens/segundo com latência inicial (TTFT) de 460 ms
  • Pesos abertos (Confidential / 140B 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

Kimi K2.7 Code · Strengths & Key Advantages

  • Janela de contexto de 262K com limite máximo de saída de 32K tokens (SWE-bench Verified 62% · GPQA Diamond 73%)
  • Taxa de transferência rápida de 75 tokens/segundo com latência inicial (TTFT) de 480 ms
  • Pesos abertos (1.5T total (40B 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.

MetricHy3Kimi K2.7 Code
Cost per Task
Estimated USD to finish one task
$0.00$0.00
Output Tokens / Task
Tokens generated per task
18K26K
Reasoning Tokens / Task
Thinking tokens burned per task
5K5K

Especificações Técnicas

Architecture, release timeline, capabilities, and modalities.

FeatureHy3Kimi K2.7 Code
Official API Model ID (SDK)hy3kimi-k2-7-code
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2026-072026-06
Knowledge CutoffApril 2026April 2026
ArchitectureEfficient Open Reasoning MoE TransformerCode-Optimized Sparse Mixture-of-Experts Transformer
ParametersConfidential / 140B MoE1.5T total (40B active MoE)
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (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 & TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 87 GB VRAM (4-bit Quantized) · 301 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 930 GB VRAM (4-bit Quantized) · 3225 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestexttext, vision
Output Modalitiestexttext

Hy3 vs Kimi K2.7 Code: Respostas Rápidas (FAQ)

Which is more intelligent, Hy3 or Kimi K2.7 Code?

Kimi K2.7 Code is more intelligent. It scores 89 compared with 81 on the composite Intelligence Index.

Which model is faster?

Hy3 is faster, generating 85 tokens per second compared with 75 tokens per second.

Which model has lower latency?

Hy3 has lower latency, with a time to first token of 0.46s compared with 0.48s.

Veredito

Tanto Hy3 (Indicado para raciocínio em cadeia de pensamento com pesos abertos e alta eficiência, arquiteturas RAG corporativas e integração escalável via API.) quanto Kimi K2.7 Code (Indicado para desenvolvimento de software e geração de código com pesos abertos auto-hospedáveis, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: Kimi K2.7 Code lidera em inteligência geral e programação, Hy3 oferece geração mais rápida e Hy3 apresenta o melhor custo-benefício em produção.

Perguntas Frequentes (FAQ)

Qual é melhor para programação e raciocínio, Hy3 ou Kimi K2.7 Code?+

Nas avaliações de inteligência e código, Kimi K2.7 Code leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.

Qual modelo tem a API mais barata, Hy3 ou Kimi K2.7 Code?+

O Hy3 custa $0.00 / $0.00 por 1M de tokens (entrada/saída) e o Kimi K2.7 Code custa $0.00 / $0.00. No custo combinado, Hy3 é mais econômico.

Qual modelo possui maior janela de contexto e velocidade?+

O Hy3 suporta 262.1K tokens de contexto contra 262.1K tokens do Kimi K2.7 Code, enquanto Hy3 entrega maior velocidade de geração.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

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 Tencent e Moonshot AI e benchmarks independentes (2026).