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What each technique speeds up
Prompt or prefix caching reduces repeated prompt processing
When a new request begins with a prefix the serving system recognizes, prompt caching can reuse previously computed attention or key-value (KV) state instead of processing that prefix from scratch. In an agent, reusable material may include system instructions, templates, and recurring context. A changing prefix, cache eviction, or provider-specific cache rules can reduce the number of useful hits. Implementations differ: the research prototype Prompt Cache describes explicit reusable prompt modules, which should not be assumed to match the controls offered by every API.
Speculative decoding targets output generation
A draft model or process proposes candidate tokens, and the target model verifies them. When enough proposals are accepted, the target can generate output with less serial decoding work. The result depends on factors such as acceptance rate, output length, and the overhead of drafting and verification. Speculative decoding does not, by itself, reuse a repeated prompt prefix. The two mechanisms are described in the same foundational paper, Speculative Decoding.
Which one fits your coding agent?
| Question | Prompt/prefix caching | Speculative decoding |
|---|---|---|
| What work does it target? | Repeated prompt prefill | Serial output decoding |
| What workload signal matters? | Long, recurring stable prefixes and a high cache hit rate | Generation is a bottleneck and drafted tokens are accepted often enough |
| What can erase the benefit? | Prefix mismatch, eviction, cache overhead, or an ineffective cache strategy | Drafting overhead or low acceptance |
| What should you measure? | Cached tokens and hit rate, prefill time, time to first token (TTFT), cost per request, and cache memory or residency | Acceptance rate or length, decode tokens per second, output latency, and compute overhead |
| What is the agent-level test? | Full task wall time, including tools and concurrent cache pressure | Full task wall time, including tools and any added serving overhead |
For example, a coding agent that repeatedly sends the same system instructions and repository guidance may have an opportunity to reuse prefix state—provided the prefix remains cacheable and resident. An agent that spends much of its model time generating long code or explanations may be a better candidate for testing speculative decoding. These are workload signals, not guarantees: profile the actual serving stack before choosing.
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What published results show—and what they do not
Prompt caching benchmarks
The 2026 paper Don’t Break the Cache evaluates prompt caching across OpenAI, Anthropic, and Google on DeepResearchBench, using more than 500 agent sessions and 10,000-token system prompts. Its authors, Elias Lumer and colleagues, report 45–80% lower API costs and 13–31% better TTFT in that benchmark. The workload is web research, not coding; those figures are not expected outcomes for a coding agent. The authors also report that strategically controlling cache blocks was more consistent than naive full-context caching, which could increase latency. Read the paper.
A separate 2024 prototype study, Prompt Cache: Modular Attention Reuse for Low-Latency Inference, reports TTFT reductions of 8× on GPU inference and 60× on CPU inference, particularly with long prompts. The evaluation used an Intel i9-13900K CPU and NVIDIA RTX 4090 and A40 GPUs. These are prototype-specific results, not a forecast for hosted coding-agent APIs. Read the paper.
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Cache residency in coding-agent work
The 2026 preprint EfficientAgent studies KV-cache offloading under concurrent agents. In its SWE-bench Verified coding-agent setup, authors Kunming Shao and colleagues report 93% fewer recomputed prompt tokens and 39% less end-to-end time when a host tier was sized to the estimated reuse working set. The paper also says offloading can speed one deployment, slow another, or make no difference. Treat its figures as results from that setup, not typical gains. Read the preprint.
These studies answer different questions. A caching benchmark does not establish a speculative-decoding result, and the cited work does not provide a controlled, same-setup coding-agent comparison of the two techniques. There is no evidence here for a numerical head-to-head winner.
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Measure the bottleneck before changing the stack
Model latency is only part of an agent task. Distinguish first-token delay from generation speed, request cost, and end-to-end wall time. Tool calls, queueing, and shared-resource contention can dominate the time a developer experiences.
- Establish a baseline. On representative coding tasks, record end-to-end task time, model-call latency, TTFT, output tokens per second, token counts, and cost per request. Track tool wait time separately where possible.
- Test caching on repeated prefixes. Measure cache hits or cached-token counts, prefill time, TTFT, and cache residency under realistic concurrency. Include requests with changed prompts and cases where a prefix may have been evicted.
- Test speculative decoding on generation-heavy work. Record draft acceptance rate or accepted length, output latency, decode throughput, and drafting or verification overhead.
- Compare under matched conditions. Hold the model, prompts, hardware or provider, concurrency, and task constant. Compare the same end-to-end metric and include any added serving overhead.
- Test the combination separately. Caching and speculative decoding target different parts of inference and may coexist in one serving stack, but their gains are not safely added together: memory use, batching, scheduling, and bottlenecks interact.
NVIDIA’s Dynamo documentation on agentic inference treats repeated-prefix reuse and cache management as parts of a broader serving system. That is a useful reminder that cache policy and concurrency matter alongside the choice of decoding method.
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How to interpret speedup claims
- TTFT is not total task time. It measures the wait for the first generated token, not the time to finish an agent task.
- Decode throughput is not necessarily user-visible latency. Faster token generation may have little effect if tools or queues dominate the task.
- Cost and speed are separate outcomes. A reduction in API cost does not by itself prove a faster coding workflow.
- Benchmark context matters. Prompts, models, hardware, providers, concurrency, and benchmark definitions affect results. A result from a research prototype or a different agent workload should not be generalized without a matching test.
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