Hy3 vs Kimi K2.7 Code
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Hy3 by Tencent and Kimi K2.7 Code by Moonshot AI 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, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 262K tokens. In latency-sensitive production environments, Hy3 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.
Changez et Comparez les Modèles
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Hy3
Raisonneur économiquede Tencent à poids ouverts Hy modèle de raisonnement développé pour abordable codage, structuré outil exécution, et sur-premises d'entreprise charges de travail.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
N/A
Kimi K2.7 Code
Spécialiste du codagedédié code engineering centrale trained à avoid unnecessary raisonnement overhead tandis que offrant pinpoint dépôt refactorisation.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
62%
Différences Clés en un Coup d’Œil
- Both models cost the same under this workload — choose on capability, not price.
- Hy3 is faster at 85 tok/s vs 75 tok/s.
| Caractéristique | Hy3 | Kimi K2.7 Code |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 81 | 89 |
Provider | Tencent | Moonshot 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 text | 262.1K tokens~393 A4 pages of text |
Max Output Tokens Per single request | 16K | 32K |
Throughput Speed Tokens generated per second | 85 tok/s | 75 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 62% |
GPQA Diamond Graduate-level reasoning | 64% | 73% |
Qui gagne où ?
At-a-glance decision frameworkEvaluations ongoing
Scores de Benchmarks Vérifiés
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.
Contamination-free competitive programming problems collected continuously over time.
Tokens emitted per second during active generation streaming.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Hy3 · Strengths & Key Advantages
- Solid 262K fenêtre de contexte avec à poids ouverts pour local privacy
- Excellent suivi des instructions et fiable JSON function calling
- Reasonable matériel empreinte deployable sur standard dual-GPU setups
Considerations & Trade-offs
- Slightly behind de pointe modèles sur difficult Olympiad-level mathematics
- moins community écosystème documentation outside de China
Kimi K2.7 Code · Strengths & Key Advantages
- 62% SWE-bench score avec reduced token verbosity et rapide génération turnaround
- Superb compréhension d'intricate multifichier git diffs et dependency trees
- élevé instruction alignment specifically tuned pour logiciel développeurs
Considerations & Trade-offs
- moins optimisé pour créatif writing ou casual ouvert-ended chatting
- Heavyweight modèle requiring d'entreprise GPU hosting
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Hy3 | Kimi K2.7 Code |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 18K | 26K |
Reasoning Tokens / Task Thinking tokens burned per task | 5K | 5K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Hy3 | Kimi K2.7 Code |
|---|---|---|
| Official API Model ID (SDK) | hy3 | kimi-k2-7-code |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-07 | 2026-06 |
| Knowledge Cutoff | April 2026 | April 2026 |
| Architecture | Efficient Open Reasoning MoE Transformer | Code-Optimized Sparse Mixture-of-Experts Transformer |
| Parameters | Confidential / 140B MoE | 1.5T 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 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) | 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 | 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) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | BF16 / 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 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, vision |
| Output Modalities | text | text |
Hy3 vs Kimi K2.7 Code: Réponses Rapides (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.
Verdict
Verdict & Model Selection Recommendation: Choose Hy3 if your priority is Private enterprise coding assistants, automated workflow agents, and internal data analysis. Its performance in demanding tasks justifies its operational token cost. Choose Kimi K2.7 Code if your workflow prioritizes Self-hosted AI software engineers, automated CI/CD code repair, and security auditing. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Questions fréquentes
How does Hy3 compare to Kimi K2.7 Code in API token pricing and cost per token?+
Both Hy3 and Kimi K2.7 Code are open-weights foundation models available at $0.00 direct API cost when self-hosted, or offered at identical baseline rates across major inference providers. Model selection depends on GPU deployment sizing and specialized benchmark performance rather than raw token price.
Which model has a larger context window: Hy3 or Kimi K2.7 Code?+
Both Hy3 and Kimi K2.7 Code offer an identical context window of 262K tokens (~197k 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: Hy3 or Kimi K2.7 Code?+
Hy3 scores at the frontier of coding benchmarks, while Kimi K2.7 Code scores 62%. For complex autonomous debugging and agentic engineering, Kimi K2.7 Code provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Hy3 and Kimi K2.7 Code?+
Hy3 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Kimi K2.7 Code provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Hy3 and Kimi K2.7 Code?+
Hy3 generates approximately 85 tokens per second, while Kimi K2.7 Code delivers 75 tokens per second. In latency-sensitive production environments, Hy3 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Hy3 and Kimi K2.7 Code to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to Hy3, and route deep architectural or complex reasoning queries to Hy3. This balanced routing strategy maximizes system throughput and answer quality.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs