Tencent
Hunyuan T1 reasoning model and Hunyuan Turbo — competitive frontier pricing
Tencent offers the Hunyuan series of large language models via its cloud platform. The 2026 lineup includes Hunyuan T1 (frontier reasoning model) and Hunyuan Turbo (fast, cost-efficient tier). Hunyuan models are optimised for Chinese and multilingual tasks, with strong performance on coding and reasoning benchmarks at competitive pricing.
Hunyuan T1 brings frontier reasoning capability to the Tencent Cloud ecosystem, while Hunyuan Turbo offers a 262K context window at competitive pricing for long-context workloads.
- Hunyuan T1 reasoning model for complex multi-step tasks
- Hunyuan Turbo for fast, cost-efficient inference
- Competitive pricing
- Strong Chinese-language performance
- Large context window (262K tokens on hy3)
- Primarily targets Chinese market
- Smaller international ecosystem
- Less third-party tooling than OpenAI
Tencent Models
| Model | Modality | Tier | Price | Cached /1M | TTFT | Throughput | Context | Intel. | Caps | |
|---|---|---|---|---|---|---|---|---|---|---|
hunyuan-turbos-20250416 Hunyuan Turbo | Chat | efficient | $0.07/ $0.28 in / out per 1M EST Jul 2026 | — | 250ms | 220 t/s | 131K | 68 | ||
hy3 Hunyuan | Chat | efficient | $0.14/ $0.56 in / out per 1M EST Jul 2026 | — | 350ms | 180 t/s | 262K | 74 | ||
hunyuan-vision-lite Hunyuan Vision | Chat | efficient | $0.14/ $0.14 in / out per 1M EST Jul 2026 | — | 400ms | 150 t/s | 8K | 65 | ||
hunyuan-t1-20250321 Hunyuan T1 | Chat | advanced | $0.56/ $2.22 in / out per 1M EST Jul 2026 | — | 2.8s | 55 t/s | 131K | 82 |
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Full comparison tableTencent LLM pricing overview
Tencent is a proprietary AI lab that develops and hosts its own large language models. Tencent offers the Hunyuan series of large language models via its cloud platform. The 2026 lineup includes Hunyuan T1 (frontier reasoning model) and Hunyuan Turbo (fast, cost-efficient tier). Hunyuan models are optimised for Chinese and multilingual tasks, with strong performance on coding and reasoning benchmarks at competitive pricing. Common use cases include Chat, Coding, Reasoning, Multilingual. Headquartered in Shenzhen, China, founded 2023. All models are accessible via a REST API compatible with standard OpenAI-style request formats, enabling drop-in integration with most LLM frameworks and orchestration tools.
Tencent vs other LLM providers
Tencent competes with OpenAI, Anthropic, Google, Groq, Together AI, Mistral, Cohere, and other inference API providers across dimensions of price, throughput, latency, context window, and model intelligence. Hunyuan T1 brings frontier reasoning capability to the Tencent Cloud ecosystem, while Hunyuan Turbo offers a 262K context window at competitive pricing for long-context workloads. Use the LLM provider comparison tool to see Tencent token pricing, latency, and throughput side-by-side with any other provider. The full LLM pricing table shows all providers ranked by input token cost, output token cost, and throughput in a single sortable view.
Why choose Tencent?
Tencent stands out because: Hunyuan T1 brings frontier reasoning capability to the Tencent Cloud ecosystem, while Hunyuan Turbo offers a 262K context window at competitive pricing for long-context workloads.. The API currently offers 4 models, spanning 1 advanced, 3 efficient tiers. For teams running high-volume inference workloads, prompt caching and batch API endpoints can reduce effective input token costs by 50–90% — check the context window cost guide for a full breakdown of caching economics.
Understanding Tencent token pricing
Tencent charges separately for input (prompt) and output (completion) tokens, priced per 1M tokens in USD. Output tokens are typically 3–5× more expensive than input tokens due to the compute cost of autoregressive generation. The cheapest {meta.name} input rate is currently $0.07/1M tokens. Prices shown are sourced from Tencent's public pricing page and updated every 15 minutes. Need help estimating your spend? Read the LLM API cost calculator guide — it covers tokens, context windows, prompt caching, and batch discounts with worked examples for RAG, chat history, and document processing workloads.
Tencent context window and model capabilities
Context window size directly affects both capability and cost — every token in the context window is charged as an input token. Tencent's largest context window is 262K tokens. For RAG and document processing workloads, longer context windows enable richer retrieval but increase per-call costs proportionally. Prompt caching — where supported — stores the KV state of repeated prefixes and charges 75–90% less for cache hits, making it the most impactful cost optimization for applications with consistent system prompts or retrieved documents. See the LLM context window cost guide for a full analysis of how context length affects your API bill.
Self-hosted vs managed inference: when Tencent makes sense
Managed inference APIs like Tencent eliminate infrastructure overhead — no GPU provisioning, driver management, or model serving stack to maintain. The trade-off is cost at scale: a single H100 at ~$2.50/hr can serve ~500K tokens/min of Llama 3.3 70B, which at Tencent API rates would cost significantly more per token. The break-even point depends on your request volume, latency requirements, and engineering capacity. For teams processing fewer than ~10M tokens/day, managed APIs are almost always cheaper when total cost of ownership is considered. Above that threshold, self-hosted inference on rented GPU compute typically wins on unit economics. Read the cheapest GPU cloud guide for a full break-even analysis. Historical Tencent token price data is available in the LLM price history charts.
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