Neither technique is universally faster. Prompt caching reduces the work of processing a repeated prompt prefix; speculative decoding aims to speed up output generation by having a draft process propose tokens for a target model to verify. For a coding agent, choose based on where time is actually going—prompt prefill, token generation, tool waits, or contention—and compare full-task latency on your own workload. The available studies do not establish a controlled, same-setup winner for coding-agent end-to-end speed.
What each technique speeds up
Prompt or prefix caching reuses prompt work
A model normally processes the input prompt before generating a response. With prefix caching, a serving system can reuse previously computed attention or key-value (KV) state when a new request starts with a matching prefix. Stable system instructions, prompt templates, and recurring context are possible candidates. This targets repeated prompt prefill, not the generation of new output tokens. The Prompt Cache paper describes explicitly modular reusable prompt segments; that should not be mistaken for a universal control surface shared by hosted APIs.
Reuse depends on the prefix matching and the cached state remaining available. Changes to the beginning of the prompt can prevent a hit, while eviction before a later request can force recomputation. Cache design also matters: retaining an entire changing context is not automatically beneficial.
Speculative decoding targets token generation
In speculative decoding, a draft model or process proposes one or more output tokens, and the target model verifies them. When enough proposed tokens are accepted, the system can reduce serial work by the target model during decoding. It does not, by itself, reuse a repeated prompt prefix. Its value depends on factors such as draft overhead, acceptance, and how much output the agent generates.
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Which is more likely to help your coding agent?
| Decision factor | Prompt or prefix caching | Speculative decoding |
|---|---|---|
| Main work targeted | Repeated prompt prefill | Serial output decoding |
| Workload signal | Long, recurring stable prefixes and a high cache-hit rate | Generation is a bottleneck and draft tokens are accepted often enough |
| Typical failure mode | Prefix mismatch, eviction, cache overhead, or an ineffective cache strategy | Draft overhead or low acceptance erases decoding savings |
| Useful measurements | Cached tokens or 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 |
| Agent-level check | Full task wall time, including tools and concurrent cache pressure | Full task wall time, including tools and serving overhead |
Use the table as a diagnosis, not a numerical head-to-head result: the cited studies do not compare both methods on the same coding-agent dataset and serving setup. A coding agent may have a long stable system prefix but spend most of its wall time waiting for repository tools—or it may generate long responses after each tool call. Those workloads call for different interventions.
How to measure the bottleneck before choosing
- Instrument the whole agent task. Record end-to-end wall time and separate model-call time from tool execution and other waits. Model latency alone can improve without materially shortening a task if tools dominate.
- Break model calls into stages. Track prompt-processing or prefill time, TTFT, and output decode speed separately. TTFT and decode tokens per second describe different parts of a response.
- For caching, inspect reuse and residency. Measure cache hits or reused tokens, misses, eviction, and the effect of concurrent requests. A nominally repeated prompt will not help if the relevant prefix differs or its state is no longer resident.
- For speculative decoding, inspect acceptance and overhead. Track how many drafted tokens are accepted and whether draft-and-verify work improves output latency under your actual model, output lengths, and concurrency.
- Compare under matched conditions. Keep model, prompts, provider or hardware, concurrency, and task constant; run representative coding tasks and compare distributions of full-task time, not just a best-case request.
Also track cost where relevant. A lower TTFT does not necessarily mean lower total cost, and a per-request improvement does not establish faster completion across a workload with different tool waits or cache pressure.
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What published results do—and do not—show
Prompt-cache findings are workload-specific
A 2026 study by Elias Lumer and colleagues evaluated prompt caching across OpenAI, Anthropic, and Google on DeepResearchBench, using more than 500 agent sessions and 10,000-token system prompts. The authors report 45–80% lower API costs and 13–31% improvements in TTFT in that evaluation; these figures are not established outcomes for coding agents. They also report that strategically controlling cache blocks was more consistent than naive full-context caching, which could increase latency. See Don’t Break the Cache.
The 2024 Prompt Cache paper by In Gim and colleagues studies modular attention reuse in a research prototype. Its evaluation reports TTFT reductions ranging from 8× on GPU inference to 60× on CPU inference, especially with long prompts. The setup included an Intel i9-13900K CPU and NVIDIA RTX 4090 and A40 GPUs. Those prototype results are not a forecast for hosted coding-agent APIs, whose cache behavior and serving systems may differ.
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Coding-agent cache results depend on the deployment
The 2026 preprint EfficientAgent studies KV-cache offloading under concurrent agents. On its SWE-bench Verified coding-agent setup, Kunming Shao and colleagues report 93% fewer recomputed prompt tokens and 39% less end-to-end time when the host tier was sized to the estimated reuse working set. The authors also describe outcomes that can speed one deployment, slow another, or make no difference. Treat the figures as a result from that setup, not a general expected gain from caching.
These results address cache behavior, not a controlled comparison against speculative decoding. Published speedups depend on prompts, models, hardware or provider, concurrency, and benchmark definitions; they cannot establish which technique wins for a particular coding agent.
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Can you use both?
Yes, they target different stages: prefix caching can reduce repeated prompt processing while speculative decoding can target output generation. A serving stack may combine them, and NVIDIA’s Dynamo agentic-inference documentation discusses repeated-prefix reuse and cache management as parts of broader agent serving.
Do not add published speedup percentages or multipliers together to estimate a combined gain. Memory use, batching, cache residency, and scheduling can interact, so measure the combined configuration against the same baseline and full-task workload.
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A practical selection rule
- Investigate prompt caching first when traces show substantial repeated prompt prefill, stable prefixes, and cache hits that persist under concurrency.
- Investigate speculative decoding first when output generation is a material share of latency and the draft process achieves enough accepted tokens to offset its overhead.
- Look beyond both when tool execution, queueing, or other waits dominate wall time; improving model inference may barely change task completion time.
- Test both together when the workload has meaningful repeated prefixes and generation bottlenecks, but judge the combined system by measured cost and end-to-end task time.
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