In recent 48‑hour observation windows, DeepSeek API received heavy traffic from diverse AI coding clients. Dax Raad, co‑founder of OpenCode, published real‑world cache hit ratio statistics across mainstream coding tools, sparking widespread discussion within the developer community. These metrics expose huge gaps in prompt‑cache implementation among AI coding assistants, even when all tools consume the same DeepSeek backend model.
Cache hit ratio, also known as prompt cache hit rate, quantifies how frequently repeated prompt segments are reused on the model service side. According to the shared dataset, ZCode takes the top spot with 98.60% cache hit ratio. Other mainstream coding clients including OpenCode V2, Cursor and Codex also maintain relatively high figures, while Claude Code / CLI lands at the bottom of this comparison table at only 89.31%.
Measured Cache Hit Ratios for DeepSeek‑Connected Coding Clients
All data comes from 48‑hour real‑production traffic statistics shared by Dax Raad.
| Client | Cache Hit Ratio |
|---|---|
| ZCode | 98.60% |
| OpenCode V2 | 97.86% |
| Cursor | 97.84% |
| Kimi Code CLI | 97.64% |
| Pi | 96.98% |
| Codex | 96.66% |
| Kilo Code | 95.73% |
| OpenCode V1 | 95.69% |
| Claude Code / CLI | 89.31% |
Most AI coding tools deliver cache hit ratios above 95% when connecting to DeepSeek endpoints. ZCode, OpenCode V2 and Cursor stand out as top‑tier performers. Even for mid‑tier tools such as Codex, the 96.66% result represents solid real‑world performance. Only Claude Code / CLI falls far behind the group average.
It is worth noting that these numbers reflect client‑side implementation quality rather than raw model capability. The same DeepSeek model serves every participant in this test. Differences arise from how each tool organizes system prompts, project context, file payloads and conversation history before sending requests. Poor context management will fragment prompt sequences and invalidate cache entries, even when you are calling identical large‑model backends. When building production‑grade AI coding workflows, developers can leverage an API gateway to standardize request formatting and optimize cache‑friendly payload structures. 4sapi offers this type of unified request orchestration capability for engineering teams.
Understanding Prompt Cache Hit Ratio for AI Coding
Many developers overlook this critical operational metric. When you operate an AI coding assistant, every API request carries far more than your immediate question. Each payload bundles multiple categories of context:
- Project‑level source code files
- Local file directory structures
- Full conversation history of current session
- Fixed system prompt instructions
- Tool‑call metadata and function definitions
Large portions of this context remain unchanged across consecutive turns. For example, most project source files do not alter between two chat rounds. Prompt cache allows the inference service to detect identical prefix segments from prior requests. Instead of re‑computing key‑value tensors for these repeated tokens, the server directly reuses cached KV‑cache results. This mechanism is called prompt caching.
Cache does not apply only to static system prompts. It works for stable code snippets, unchanged file lists and persistent tool schemas. Only modified segments will go through full token processing. For heavy‑load coding scenarios, this optimization generates measurable differences in latency and token expenses.
Why Cache Hit Ratio Matters for Engineering Workloads
AI coding workflows consume massive token volumes. Tools like Cursor, Codex, OpenCode and Claude Code frequently load dozens of project files into context windows within a single task. If every request re‑transmits the entire codebase without cache reuse, input‑token costs will surge rapidly.
When cache hits occur, three tangible benefits appear:
- Reduced input‑token billing: Cached segments are charged at heavily discounted rates on DeepSeek’s pricing model. Uncached input tokens can cost dozens‑fold higher than cached ones.
- Improved response latency: The inference engine skips prefill computation for cached content, so the model starts generating output tokens faster.
- Enhanced stability for large‑code tasks: Faster prefill reduces request timeout risks when processing multi‑file repositories.
For long‑running agent sessions that iterate over code repositories, even a few percentage‑point increase in cache hit ratio creates significant cumulative cost savings. That explains why modern AI coding tool vendors invest heavily in context‑management modules.
Cost Disparities Introduced by DeepSeek Prompt Cache
DeepSeek implements differentiated pricing for cached and non‑cached input tokens. Community discussions confirm that uncached input tokens can cost up to dozens‑times more than cached equivalents. The core takeaway is straightforward: higher cache hit ratio equals lower overall API expense.
We can perform rough comparative calculation based on real‑world measurement data. Compare ZCode (98.60%) against Claude Code / CLI (89.31%). Given identical total token volume, the client with superior cache utilization drastically cuts effective input‑token spending. This gap grows wider for long sessions spanning hundreds of message turns.
For this reason, modern AI coding tools prioritize multiple technical directions: context‑window management, prompt reuse, conversation compression and file‑indexing logic. These modules do not improve model output quality directly. Their purpose is to preserve stable prompt prefixes so the backend can trigger cache hits.
Technical Drivers Behind ZCode’s 98.60% Cache Hit Ratio
Official technical details of ZCode’s optimization logic are not fully public. From general AI‑coding engineering practice, three major factors contribute to high cache hit rates:
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Stable system prompt Minimal variation in system‑prompt segments across requests strongly favors cache matching. If the tool injects dynamic timestamps, random identifiers or shifting instruction blocks inside the system prompt on every round, the prefix changes constantly and breaks cache continuity. Keeping system‑prompt payload invariant maximizes cache reusability.
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Predictable context concatenation order ZCode loads project metadata, file contents, user instructions and tool‑state blocks in fixed sequence. Deterministic ordering of context segments preserves identical prompt prefixes across multiple requests. Randomly reordered context blocks will break cache matches even when the logical content stays the same. Fixed‑sequence assembly is a simple yet effective engineering practice.
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Intelligent code‑context filtering High‑performance AI IDE clients avoid dumping the entire repository in every request. They build local indexes, select relevant source files dynamically and only feed contextually necessary code snippets into each API payload. This reduces total token throughput and prevents unnecessary mutation of prompt prefixes. Poor implementations send full file trees in every turn, introducing frequent context changes and hurting cache statistics.
Community Feedback on Released Benchmark Data
After Dax Raad shared the cache‑hit dataset, multiple threads emerged across developer forums. Several participants voiced surprise that Pi did not reach 100% cache utilization as many had anticipated. Others commented that performance gaps between Pi and Codex turned out smaller than expected from subjective product experience.
Codex achieved 96.66% cache hit ratio. While it does not land within top‑3 rankings, this figure remains a strong real‑world result for end‑user‑facing coding software. The benchmark highlights that cache performance forms a hidden dimension of product competitiveness which most end‑users cannot observe directly.
AI Coding Tools Enter the Era of Hidden‑Indicator Competition
Historically, developers evaluated AI coding assistants mainly based on visible factors: raw model capability, code‑generation accuracy, UI experience and plugin compatibility. As AI agents gain popularity for repository‑scale engineering tasks, new non‑functional competition dimensions rise in importance.
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Token cost control Given equivalent task completion quality, solutions that consume fewer tokens deliver lower long‑term operating expenditure. For teams running large‑volume agent workloads, monthly API bills become a critical constraint.
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Large‑repository context management When processing multi‑thousand‑file codebases, how tools organize, filter and deliver source‑code context directly determines whether the AI can correctly understand project logic. Poor context handling leads to hallucinations and broken code output regardless of underlying model strength.
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Prompt‑cache optimization capability Cache hit ratio is a measurable proxy for above‑two capabilities. Higher cache hit ratio correlates with lower latency, reduced cost and more stable user experience. Optimized cache behavior cannot replace strong model capability, yet excellent models get wasted when client‑side context logic remains sub‑optimal.
Real‑world production observations repeatedly demonstrate this point. Teams building agent workflows often invest substantial engineering effort on request‑layer optimizations rather than only switching to newer model versions. Standardizing request schemas, deduplicating context payloads and maintaining stable prompt prefixes bring tangible operational gains. Engineering teams can unify these request‑processing workflows via centralized infrastructure such as 4sapi.
Conclusion
The DeepSeek cache‑hit benchmark reveals a critical industry truth: competition among AI coding assistants is no longer purely about model‑side capability. Outstanding AI coding products must combine powerful model invocation, sophisticated context‑management logic and well‑tuned prompt‑cache strategies to achieve low token‑cost operation.
ZCode’s 98.60% cache hit ratio represents a notable real‑world benchmark. Mainstream tools including OpenCode and Cursor also maintain competitive cache performance. By contrast, Claude Code / CLI shows considerable room for improvement on this metric.
For developers building custom AI coding agents, cache hit ratio should become one of your core operational KPIs. You should continuously monitor this metric in production. Even if you adopt state‑of‑the‑art foundation models, badly‑structured request payloads will drive up latency and billing costs while degrading end‑user experience.
The industry is evolving fast. As more engineering teams run AI agents against real‑world repositories, invisible backend metrics such as prompt‑cache efficiency will grow more influential in product differentiation.
