Hunyuan Large V2 vs Claude Opus 4.8
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Hunyuan Large V2 by Tencent and Claude Opus 4.8 by Anthropic 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, Hunyuan Large V2 holds a notable pricing advantage for token ingestion. On output generation, Hunyuan Large V2 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Claude Opus 4.8 provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, Hunyuan Large V2 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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Hunyuan Large V2
深度推理模型Tencent 推出的 Hunyuan Large V2 人工智能模型，具备 512K 上下文窗口，输入价格为 $0.7/百万 Token，输出价格为 $2.8/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.70 / 1M
$2.80 / 1M
512K tokens
N/A
Claude Opus 4.8
深度推理模型Anthropic 推出的 Claude Opus 4.8 人工智能模型，具备 1M 上下文窗口，输入价格为 $5/百万 Token，输出价格为 $25/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$5.00 / 1M
$25.00 / 1M
1M tokens
80.8%
核心差异一览
- Hunyuan Large V2 is 8.4x cheaper for API usage (input + output tokens combined).
- Claude Opus 4.8 has a larger context window (1M vs 512K tokens).
- Hunyuan Large V2 is faster at 80 tok/s vs 64 tok/s.
| Feature | Hunyuan Large V2 | Claude Opus 4.8 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 85 | 67 |
Provider | Tencent | Anthropic |
Input Price Cost per million input tokens | $0.70 / 1M | $5.00 / 1M |
Output Price Cost per million output tokens | $2.80 / 1M | $25.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.47 / 1M | $3.85 / 1M |
Context Window Maximum input + output tokens | 512K tokens~768 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 16K | 128K |
Throughput Speed Tokens generated per second | 80 tok/s | 64 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 80.8% |
GPQA Diamond Graduate-level reasoning | 66% | 78.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.07 / 1M | $0.50 / 1M |
各项指标谁更胜一筹？
At-a-glance decision frameworkEvaluations ongoing
权威基准测试得分对比
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.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Tokens emitted per second during active generation streaming.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Hunyuan Large V2 · Strengths & Key Advantages
- 由 Tencent 研发的高精度 High-Scale Sparse Mixture-of-Experts Transformer 架构，支持 512K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.7/1M，输出 $2.8/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Claude Opus 4.8 · Strengths & Key Advantages
- 由 Anthropic 研发的高精度 Frontier Hybrid Cognitive Architecture 架构，支持 1M 上下文窗口
- 极具竞争力的 API 定价（输入 $5/1M，输出 $25/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Hunyuan Large V2 | Claude Opus 4.8 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.22 | $5.49 |
Output Tokens / Task Tokens generated per task | 22K | 72K |
Reasoning Tokens / Task Thinking tokens burned per task | 7K | 37K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Hunyuan Large V2 | Claude Opus 4.8 |
|---|---|---|
| Official API Model ID (SDK) | hunyuan-large-v2 | claude-opus-4-8-20260501 |
| API Protocol | Tencent REST API (OpenAI-Compatible) | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2026-05 | 2026-05 |
| Knowledge Cutoff | February 2026 | March 2026 |
| Architecture | High-Scale Sparse Mixture-of-Experts Transformer | Frontier Hybrid Cognitive Architecture |
| Parameters | 500B total (30B active MoE) | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.7/1M in) across full 512K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.07/1M tokens | $0.5/1M cached read (90% off); 5-min default & 1-hour extended TTL |
| 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 | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | Custom Model Tuning via Amazon Bedrock & Prompt Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Tencent Production Safety & Alignment Verified | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text | text, vision |
| Output Modalities | text | text |
Hunyuan Large V2 vs Claude Opus 4.8: 常见问题解答 (FAQ)
Which is more intelligent, Hunyuan Large V2 or Claude Opus 4.8?
Hunyuan Large V2 is more intelligent. It scores 85 compared with 67 on the composite Intelligence Index.
Which model is faster?
Hunyuan Large V2 is faster, generating 80 tokens per second compared with 64 tokens per second.
Which model is cheaper to run?
Hunyuan Large V2 is cheaper at $0.47 per 1M tokens blended, compared with $3.85 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Hunyuan Large V2 has lower latency, with a time to first token of 0.48s compared with 1.65s.
Which model has a larger context window?
Claude Opus 4.8 has a larger context window, supporting 1M tokens compared with 512K tokens.
综合结论
Verdict & Model Selection Recommendation: Choose Hunyuan Large V2 if your priority is Social chatbots, creative writing, game NPC dialogue, and extensive document summarization. Its performance in demanding tasks justifies its operational token cost. Choose Claude Opus 4.8 if your workflow prioritizes Frontier AI research, multi-agent systems orchestration, and enterprise software engineering. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
常见问题 (FAQ)
How does Hunyuan Large V2 compare to Claude Opus 4.8 in API token pricing and cost per token?+
Hunyuan Large V2 costs $0.70 per 1M input tokens and $2.80 per 1M output tokens, whereas Claude Opus 4.8 costs $5.00 input and $25.00 output. Overall, Hunyuan Large V2 is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Hunyuan Large V2 or Claude Opus 4.8?+
Hunyuan Large V2 supports a context window of 512K tokens (~384k words), while Claude Opus 4.8 supports 1,000K tokens (~750k words). Therefore, Claude Opus 4.8 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Hunyuan Large V2 or Claude Opus 4.8?+
Hunyuan Large V2 scores at the frontier of coding benchmarks, while Claude Opus 4.8 scores 80.8%. For complex autonomous debugging and agentic engineering, Claude Opus 4.8 provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Hunyuan Large V2 and Claude Opus 4.8?+
Hunyuan Large V2 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Claude Opus 4.8 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Hunyuan Large V2 and Claude Opus 4.8?+
Hunyuan Large V2 generates approximately 80 tokens per second, while Claude Opus 4.8 delivers 64 tokens per second. In latency-sensitive production environments, Hunyuan Large V2 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Hunyuan Large V2 and Claude Opus 4.8 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Hunyuan Large V2 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Claude Opus 4.8. This tiered routing pattern cuts overall API bills by up to 70%.
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.
数据来源: GetInfoUs Independent AI Benchmarks & Official API Specs