Hy3 vs Hy3 preview
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Hy3 by Tencent and Hy3 preview by Tencent 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 preview 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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Hy3
Budget ReasonerTencent's open-weights Hy reasoning model engineered for affordable coding, structured tool execution, and on-premises enterprise workloads.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
N/A
Hy3 preview
PreviewEarly preview release of the Hy3 reasoning architecture allowing developers to test next-generation instruction following and tool loops.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
N/A
Key Differences at a Glance
- Both models cost the same under this workload — choose on capability, not price.
- Hy3 preview is faster at 88 tok/s vs 85 tok/s.
| Feature | Hy3 | Hy3 preview |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 81 | 79 |
Provider | Tencent | Tencent |
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 | 8K |
Throughput Speed Tokens generated per second | 85 tok/s | 88 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
GPQA Diamond Graduate-level reasoning | 64% | N/A |
Who Wins Where?
At-a-glance decision frameworkEvaluations ongoing
Empirical Head-to-Head Benchmark Scores
Official evaluation metrics verified by independent third-party research labs.
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.
Tokens emitted per second during active generation streaming.
Latency & Response Time
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Real-Time Interactive Token Cost Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Hy3 · Strengths & Key Advantages
- Solid 262K context window with open weights for local privacy
- Strong instruction following and reliable JSON function calling
- Reasonable hardware footprint deployable on standard dual-GPU setups
Considerations & Trade-offs
- Slightly behind frontier models on difficult Olympiad-level mathematics
- Less community ecosystem documentation outside of China
Hy3 preview · Strengths & Key Advantages
- Early access to Tencent updated MoE reasoning architecture
- Free open-weights testing for enterprise evaluation
- Good baseline performance across Chinese and English tasks
Considerations & Trade-offs
- Preview build with occasional experimental edge-case variations
- Superseded by the final production Hy3 release
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Hy3 | Hy3 preview |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 18K | 15K |
Reasoning Tokens / Task Thinking tokens burned per task | 5K | 3K |
Technical Specifications
Architecture, release timeline, capabilities, and modalities.
| Feature | Hy3 | Hy3 preview |
|---|---|---|
| Official API Model ID (SDK) | hy3 | hy3-preview |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-07 | 2026-04 |
| Knowledge Cutoff | April 2026 | February 2026 |
| Architecture | Efficient Open Reasoning MoE Transformer | Preview Open Reasoning MoE Transformer |
| Parameters | Confidential / 140B MoE | Confidential / 140B 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) · 87 GB VRAM (4-bit Quantized) · 301 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 87 GB VRAM (4-bit Quantized) · 301 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 |
Hy3 vs Hy3 preview: Quick Answers
Which is more intelligent, Hy3 or Hy3 preview?
Hy3 is more intelligent. It scores 81 compared with 79 on the composite Intelligence Index.
Which model is faster?
Hy3 preview is faster, generating 88 tokens per second compared with 85 tokens per second.
Which model has lower latency?
Hy3 preview has lower latency, with a time to first token of 0.44s compared with 0.46s.
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 Hy3 preview if your workflow prioritizes Early benchmarking, architectural evaluation, and experimental agent testing. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Frequently asked questions
How does Hy3 compare to Hy3 preview in API token pricing and cost per token?+
Both Hy3 and Hy3 preview 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 Hy3 preview?+
Both Hy3 and Hy3 preview 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 Hy3 preview?+
Hy3 scores at the frontier of coding benchmarks, while Hy3 preview scores competitively for software generation. For complex autonomous debugging and agentic engineering, Hy3 provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Hy3 and Hy3 preview?+
Hy3 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Hy3 preview provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Hy3 and Hy3 preview?+
Hy3 generates approximately 85 tokens per second, while Hy3 preview delivers 88 tokens per second. In latency-sensitive production environments, Hy3 preview provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Hy3 and Hy3 preview 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 preview, 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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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.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs