AI tool comparison
Claude Opus 4.7 vs Tencent Hy3 Preview
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
AI Models
Claude Opus 4.7
Anthropic's flagship model with task budgets for disciplined agentic work
75%
Panel ship
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Community
Paid
Entry
Claude Opus 4.7, released April 16, 2026, is Anthropic's strongest model to date and introduces a meaningful new primitive for agentic work: task budgets. A task budget gives Claude a token target for the entire agentic loop — thinking, tool calls, tool results, and final output — with a running countdown that lets the model prioritize and wind down gracefully rather than running out of context mid-task. Beyond task budgets, Opus 4.7 ships with substantially better vision at higher resolutions, improved creative output quality (better interfaces, slides, and docs), and gains on the hardest software engineering tasks where Opus 4.6 struggled to maintain context across long refactors. Pricing stays flat at $5/1M input and $25/1M output. Available day-one across Claude Pro, API, Amazon Bedrock, Vertex AI, Microsoft Foundry, Claude Code, Cursor, and GitHub Copilot, Opus 4.7 cements Anthropic's position as the go-to model for serious agentic workloads — particularly long-horizon coding sessions that previously needed close human supervision.
AI Models
Tencent Hy3 Preview
295B MoE open weights — China's most efficient frontier model yet
75%
Panel ship
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Community
Paid
Entry
Tencent open-sourced Hy3 Preview on April 23, 2026 — the first model to emerge from the company's rebuilt AI infrastructure, and its most credible challenge to frontier closed models to date. With 295 billion total parameters but only 21 billion active at inference time (plus 3.8B MTP layer parameters), it's a Mixture-of-Experts architecture that punches far above its compute weight. The model supports up to 256K context and is available via Hugging Face, ModelScope, and GitCode under the Tencent Hy Community License. On coding benchmarks, Hy3 scores 74.4% on SWE-bench Verified, 54.4% on Terminal-Bench 2.0, and 67.1% on BrowseComp — placing it firmly in the same tier as top models from Anthropic and OpenAI. Tencent claims a 40% efficiency improvement over its predecessor Hunyuan models, and pricing through Tencent Cloud TokenHub is aggressive: RMB 1.2 per million input tokens. A free two-week window at launch via OpenRouter made it widely accessible immediately. The model was led by a team that includes former OpenAI researchers and has already been deployed across Tencent's core products — WeChat, Yuanbao, and QQ. That production integration is a meaningful signal: this isn't a benchmark vanity release. For developers who need a powerful, cost-efficient reasoning and agentic model with actual open weights, Hy3 Preview is one of the most interesting drops of April 2026.
Reviewer scorecard
“Task budgets are the most useful new feature in a model release this year. I can now hand off a 4-hour refactor with confidence that Claude won't run off the rails or stall out at 80%. The hard coding gains are real — agentic loops on big codebases feel qualitatively different.”
“21B active params with 295B total — this is genuinely practical to deploy on reasonable hardware while matching models 10x the inference cost. The 256K context and strong SWE-bench score make it a legitimate option for agentic coding pipelines. I'd use this today.”
“At $25/1M output tokens, a single complex agentic loop can easily cost $5-10. Task budgets help, but they're a bandaid on the fundamental cost problem. For most teams, Sonnet 4.6 delivers 80% of the capability at 20% of the price.”
“The Tencent Hy Community License is not Apache 2.0 or MIT — read it carefully before using this in production. There are usage restrictions that could bite commercial deployments. Also, benchmark scores look great, but independent evals of Chinese labs' models have historically diverged from self-reported numbers.”
“Task budgets represent a real shift in how we think about agent control — not 'stop the agent if it goes wrong' but 'give the agent enough rope to finish, not enough to hang itself.' This mental model will propagate across the industry.”
“The MoE efficiency race is the actual story here — we're getting frontier-class capability at a fraction of the activation cost. Hy3 is proof that the compute-vs-capability Pareto frontier keeps moving. Open weights with real deployment signals (WeChat at scale) is a combination that matters.”
“The higher-resolution vision and tasteful output quality improvements are immediately noticeable in design-adjacent tasks. Generating polished slides and landing pages feels less like prompting a robot and more like briefing a designer.”
“Strong visual coding capabilities and multimodal understanding make this genuinely useful for design-to-code workflows. The health image analysis and product comparison use cases already deployed in Yuanbao show real-world creative utility beyond pure benchmark games.”
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