--- title: "AI Coding Assistants With Prompt Caching: Test Reuse." description: "Test AI coding assistants with prompt caching on codebase work. Measure cache hits, latency, cost, and invalidation separately from product memory." canonical: "https://scalewithsearch.com/articles/ai-coding-assistants-prompt-caching-large-codebases" date: "2026-08-24" modified: "2026-09-19" --- ## Site navigation - [Scale With Search](https://scalewithsearch.com/) - Real estate - Real estate - [Real estate](https://scalewithsearch.com/for/real-estate) - Work - Start here - [Send your brief](https://scalewithsearch.com/work#send-your-brief) - [Prepare your six-question brief](https://scalewithsearch.com/work#prepare-your-six-question-brief) - Build - [Site build, content library with SEO, signal desk](https://scalewithsearch.com/work) - For your business - Trades and home services - [Auto body and collision shops](https://scalewithsearch.com/for/auto-body-and-collision-shops) - [Foundation and home repair contractors](https://scalewithsearch.com/for/foundation-and-home-repair) - [Garage door and fencing contractors](https://scalewithsearch.com/for/garage-door-and-fencing-contractors) - 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[The Complete Guide to Leaving Vendor AI Memory](https://scalewithsearch.com/articles/leaving-vendor-ai-memory-guide) - Articles by cluster - [Business memory](https://scalewithsearch.com/articles/business-memory-for-ai-agents-guide) - [Agent governance](https://scalewithsearch.com/articles/ai-agent-governance-guide-small-business) - [Migration and ownership](https://scalewithsearch.com/articles/leaving-vendor-ai-memory-guide) - For machines - [llms.txt](https://scalewithsearch.com/llms.txt) - [llms-full.txt](https://scalewithsearch.com/llms-full.txt) - [Machine view](https://scalewithsearch.com/?view=machine) - Company - Evidence - [Proof](https://scalewithsearch.com/proof) - Company - [About](https://scalewithsearch.com/about) # Which AI Coding Assistants Support Prompt Caching for Large Codebases. A team pays to resend the same tool definitions, repository map, and coding rules on every agent turn. The product also advertises "memory," so the team assumes repeated context is cheaper. Nobody can show a cache-hit field, stable prefix, or cost receipt. Prompt caching, repository indexing, chat history, and project memory are different mechanisms. A coding assistant may use provider caching internally without exposing it. An API workflow may expose cached tokens while offering no repository index. Test the route you will pay for. ## Prompt caching reuses processed input prefixes OpenAI says eligible API requests receive automatic prompt caching. Cache hits depend on exact prefix matches, so stable instructions and examples belong before variable request content. The API exposes cached-token usage, with newer models adding cache-write evidence. [OpenAI: Prompt caching](https://developers.openai.com/api/docs/guides/prompt-caching) Anthropic documents prompt caching through cache breakpoints on prompt content. Its API returns cache creation and cache read token fields so developers can distinguish writes from later reuse. [Anthropic: Prompt caching](https://platform.claude.com/docs/en/build-with-claude/prompt-caching) Google documents implicit and explicit context caching for Gemini API routes. Its current API surfaces cached-token usage and describes prefix stability as a factor in implicit hits. [Google: Context caching](https://ai.google.dev/gemini-api/docs/caching/) These are provider capabilities. They do not prove that a named editor or agent plan passes savings through, uses the same route, or exposes the receipt. ## Repository indexing and memory are not cache evidence A repository index helps retrieve relevant code without sending every file in every prompt. A project rule injects durable instructions. Chat history preserves prior turns. A memory feature may store learned facts. For comparing project continuity in the coding assistants themselves, read [Cursor Continuity vs Claude Code: Project Memory.](/articles/claude-code-vs-cursor-project-memory) Prompt caching reduces repeated processing when the provider sees a reusable prompt prefix. The mechanisms can work together, but they should not share one label in a buyer scorecard. GitHub documents that Copilot uses prompt caching for service quality and latency across hosted model routes. That confirms platform use, but it does not give every end user a per-turn cache-hit and cost field. [GitHub: Hosting of models for Copilot](https://docs.github.com/en/copilot/reference/ai-models/model-hosting) Ask each vendor which layer it controls, what telemetry the plan exposes, and whether savings affect the buyer's bill. ## Define one repeated repository workload Choose a stable repository fixture. Prepare a fixed system instruction, tool schema, repository brief, and selected source set. Then define five variable questions that depend on the same prefix. Examples include locating a validation path, explaining two consumers, proposing a bounded change, generating a test, and reviewing the resulting diff. Keep output limits consistent. Use the same model and route for cold and warm runs. Record timestamps, usage fields, latency, and price calculation. The [coding-agent reliability scorecard](/articles/ai-agent-reliability-metrics-small-business) should remain the quality gate. A cache win matters only when task success stays acceptable. ## Inspect `prompt-cache-trial.csv` **Synthetic example, not measured product performance.** The rows illustrate evidence fields. Replace token counts and timings with your own raw usage receipts before calculating savings. ```csv assistant,provider,route,stable_prefix_hash,cold_input,warm_input,cache_write,cache_read,latency_ms,invalidation,cost_evidence API-A,OpenAI,responses,sha256:TEST-A,18000,18200,16000,0,4200,none,usage object API-A,OpenAI,responses,sha256:TEST-A,18000,18150,0,16000,2100,none,usage object EDITOR-B,unknown,subscription,sha256:TEST-A,unknown,unknown,unknown,unknown,1800,none,invoice only ``` Use `unknown` when the product does not expose a field. Do not turn a faster second response into a claimed cache hit. Network, server load, session reuse, or a smaller retrieved context can also change latency. Save raw usage objects where the API provides them. The [run receipt pattern](/articles/receipt-is-part-of-the-output) prevents a summary spreadsheet from becoming the only evidence. ## Run one cold turn and five warm turns Start with no reusable cache for the test key where the provider supports that distinction. Send the stable prefix and first question. Record cache-write and read fields. Then send five requests with the exact reusable prefix and variable content at the end. 1. Confirm the prefix hash remains identical. 2. Record input, output, cached, and cache-write tokens. 3. Record time to first token and total latency if available. 4. Calculate cost from the current provider price sheet. 5. Run the expected answer checks. 6. Repeat at a different interval if cache retention matters. 7. Separate failed requests and retries from successful turns. For a subscription coding assistant, collect the telemetry it exposes. If it exposes none, report that cache behavior is not buyer-verifiable on that route. ## Invalidate the cache deliberately Change one early instruction in the stable prefix. That should alter the prefix hash and may prevent reuse after the changed point. Change only the final user question in another run. That should preserve the reusable prefix. Next, change a repository source file. A provider cache can still match old prompt bytes if the integration sends old content. The assistant or retrieval layer must assemble the current source before caching can help. Test these cases: - changed tool schema; - changed system instruction; - reordered static files; - changed repository brief; - changed source after the cache boundary; - same source bytes with a different filename; - expired cache or delayed warm request. Record quality after each change. A cheap stale answer is a failure. ## Compare cost without hiding quality or storage Provider caching prices can include separate write, read, and storage terms. They can also change by model and retention mode. Use the current official price sheet on the test date. Count uncached input, cache writes, cache reads, output, retries, and any cache storage. Include the calls used to build repository context. Do not compare a direct API bill with a flat subscription as though their unit economics are identical. Latency savings can matter even when the invoice does not. Measure the workflow time from request to accepted result, not only model response time. The [DIY versus reviewed build decision](/articles/diy-obsidian-claude-code-vs-scoped-build) adds operator and review time to the tool cost. ## Check tenancy and data controls A cache is also a data-handling feature. Confirm whether the provider isolates cached material by organization or project, how long it remains eligible for reuse, and which retention mode the route uses. Do not enable a longer cache solely for savings when it conflicts with the workload's data policy. Use separate test keys or projects for unrelated clients. Never create a shared prefix that combines repositories with different access rules. A cache hit must not become a reason to broaden context. Record the provider documentation and configuration date beside the cost result. If the route changes its model host, retention, or usage schema, invalidate the procurement conclusion and rerun the fixture. ## Decide whether caching changes the tool choice Caching matters most when the workflow repeatedly sends a large, stable prefix and shorter variable requests. It matters less when repository context changes every turn, prompts are small, or the product already retrieves only a compact relevant set. Require three proofs before making a savings claim: 1. provider or product documentation establishes the mechanism; 2. runtime telemetry shows cache writes or reads on the tested route; 3. the cost and quality calculation shows a useful net result. If the product hides telemetry, treat prompt caching as an implementation detail. Choose it on accepted task performance, total price, latency, security, and continuity. ## Approval and stopping boundary This trial may run against a synthetic repository, call approved developer APIs, collect usage telemetry, and calculate estimated cost. It may use disposable cache keys and delete explicit test caches when supported. It stops before sending proprietary source to an unapproved provider, changing production prompts, enabling extended retention, raising spend limits, or altering team subscriptions. Data owners approve provider access. Billing owners approve paid tests and plan changes. Repository owners approve source selection. If the route does not expose cache evidence, report `unknown` rather than inferring a hit. ## Sources - [OpenAI: Prompt caching](https://developers.openai.com/api/docs/guides/prompt-caching) - [Anthropic: Prompt caching](https://platform.claude.com/docs/en/build-with-claude/prompt-caching) - [Google: Context caching](https://ai.google.dev/gemini-api/docs/caching/) - [GitHub: Hosting of models for Copilot](https://docs.github.com/en/copilot/reference/ai-models/model-hosting) ## Questions about Which AI Coding Assistants Support Prompt Caching for Large Codebases? ### Does prompt caching save meaningful money on AI coding agents? It can when repeated requests preserve a cacheable prefix, but the result must be measured on the actual repository workload. Record cold and warm input tokens, cache reads and writes, latency, cost, output quality, and invalidation across repeated turns. ### Is repository indexing the same thing as prompt caching? No, indexing retrieves repository material, while prompt caching reuses processed input prefixes under provider-specific rules. Product memory is another separate layer, so verify cache metrics rather than inferring a cache hit from faster or more context-aware output. ### How should I test prompt caching on a large codebase? Run one cold turn and several identical warm turns with a stable prefix, then change an early instruction or repository block to force invalidation. Compare cost and latency without ignoring correctness, tenancy, storage, or whether the tool silently changes the prompt layout. ## Related: Testing and Acceptance - [Claude Code vs ChatGPT for a Large Codebase: Repository Context, Tests, and Handoff](/articles/claude-code-vs-chatgpt-large-codebase) - [Compare how six AI coding assistants keep project memory between sessions](/articles/ai-coding-assistants-memory-comparison) - [Claude-Mem Alternatives for Coding Agents: Score the Memory Layer](/articles/claude-mem-alternatives-coding-agents) ---- ```text .|########||. .|########||. .|########||. |##||. .||##|. |##||. .||##|. |##||. .||##|. |#|. .|#|. |#|. .|#|. |#|. .|#|. |#| |#| |#| |#| |#| |#| .#| |#. .#| |#. .#| |#. .#. .#| .#. .#| .#. .#| |#. .#| |#. .#| |#. .#| |# ...... #| |# ...... #| |# ...... #| .# ||#########| #| .# ||#########| #| .# ||#########| #| .#. |######||######|. .#. .#. |######||######|. .#. .#. |######||######|. .#. #. .##|###|##|#|######| .# #. .##|###|##|#|######| .# #. .##|###|##|#|######| .# || |##|#||||||||||||#||#| || || |##|#||||||||||||#||#| || || |##|#||||||||||||#||#| || # |#||||||||||||||||||||#| #. # |#||||||||||||||||||||#| #. # |#||||||||||||||||||||#| #. || |#||||||||||||||||||||||#| || || |#||||||||||||||||||||||#| || || |#||||||||||||||||||||||#| || # .#||||||||||||||||||||||||#| # # .#||||||||||||||||||||||||#| # # .#||||||||||||||||||||||||#| # || ....|||#||||||##|#|||#|#||##||||....|. || || ....|||#||||||##|#|||#|#||##||||....|. || || ....|||#||||||##|#|||#|#||##||||....|. || #. . ....|# ....#||| ||| .#||| ....#. .# #. . ....|# ....#||| ||| .#||| ....#. .# #. . ....|# ....#||| ||| .#||| ....#. .# # . ||||#| .#####|| . .#| .#||| ||||# #. # . ||||#| .#####|| . .#| .#||| ||||# #. # . ||||#| .#####|| . .#| .#||| ||||# #. .| ||||| #. |#|||||. || #. |#||. .||||| || .| ||||| #. |#|||||. || #. |#||. .||||| || .| ||||| #. |#|||||. || #. |#||. .||||| || || |.... #. ....|#. |. ...|. ....|| || || |.... #. ....|#. |. ...|. ....|| || || |.... #. ....|#. |. ...|. ....|| || #. .||||||##||||||##||####|||||||#||||||#| .# #. .||||||##||||||##||####|||||||#||||||#| .# #. .||||||##||||||##||####|||||||#||||||#| .# # .||####|###########################|. # # .||####|###########################|. # # .||####|###########################|. # # .#||||||.#.|| # |. ..# #| #|||||#| # # .#||||||.#.|| # |. ..# #| #|||||#| # # .#||||||.#.|| # |. ..# #| #|||||#| # # .#|||||| .. |# ## |#| ...#|#|||#| # # .#|||||| .. |# ## |#| ...#|#|||#| # # .#|||||| .. |# ## |#| ...#|#|||#| # # .#|||#|# .# .#| #| ##|.#..#|||||#| # # .#|||#|# .# .#| #| ##|.#..#|||||#| # # .#|||#|# .# .#| #| ##|.#..#|||||#| # # .#||||||############|######|||||#| # # .#||||||############|######|||||#| # # .#||||||############|######|||||#| # # |...||#||||||#||||#|#||||||#|||||#||| |.| # # |...||#||||||#||||#|#||||||#|||||#||| |.| # # |...||#||||||#||||#|#||||||#|||||#||| |.| # #. .. ||||# .|||##|. |#| .|| || .|||#. #| # .# #. .. ||||# .|||##|. |#| .|| || .|||#. #| # .# #. .. ||||# .|||##|. |#| .|| || .|||#. #| # .# || | ...|| ...##| | #| .|. |. ##### .. .| || || | ...|| ...##| | #| .|. |. ##### .. .| || || | ...|| ...##| | #| .|. |. ##### .. .| || || ||||| || ||||#| . |. |. |#. ||||| |#| || || || ||||| || ||||#| . |. |. |#. ||||| |#| || || || ||||| || ||||#| . |. |. |#. ||||| |#| || || .# |.....#|....|#.||||.|||##.|#|....||.#||.#. #. .# |.....#|....|#.||||.|||##.|#|....||.#||.#. #. .# |.....#|....|#.||||.|||##.|#|....||.#||.#. #. #.|||||||#############################| ||| .# #.|||||||#############################| ||| .# #.|||||||#############################| ||| .# || ##||#||#||||||#||#||#||#||##. || || ##||#||#||||||#||#||#||#||##. || || ##||#||#||||||#||#||#||#||##. || # .#|||||||||#||#|||||||||||#| # # .#|||||||||#||#|||||||||||#| # # .#|||||||||#||#|||||||||||#| # || |#||||||||||||||||||||||#| || || |#||||||||||||||||||||||#| || || |#||||||||||||||||||||||#| || .# |#||||||||||||||||||||#| #. .# |#||||||||||||||||||||#| #. .# |#||||||||||||||||||||#| #. || |#||||||||||||||||||#| || || |#||||||||||||||||||#| || || |#||||||||||||||||||#| || #. .######|#|#########| .# #. .######|#|#########| .# #. .######|#|#########| .# .#. |#####||||#####| .#. .#. |#####||||#####| .#. .#. |#####||||#####| .#. |# ||########|| #. |# ||########|| #. |# ||########|| #. |# ...... #| |# ...... #| |# ...... #| |#. .#| |#. .#| |#. .#| |#. .#. |#. .#. |#. .#. .#| |#. .#| |#. .#| |#. |#| |#| |#| |#| |#| |#| .|#|. .|#|. .|#|. .|#|. .|#|. .|#|. |##||. .||##| |##||. .||##| |##||. .||##| .||########||. .||########||. .||########||. 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