Hey there! Great question — you’re definitely not alone in running into this.
Unfortunately, Web Search isn’t currently supported in the Batch API for /v1/responses, which is why you’re getting the “Web search is not supported in the Batch API” error. The Batch endpoint is designed for fully self-contained prompts and doesn’t allow dynamic tool execution (like live search or code interpreter) for security and performance reasons.
That said, you can still achieve something close to your goal — web-grounded batch answers — with a small architectural tweak:
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Pre-fetch your web data first: Before sending your batch job, run a separate process (or even a small script) that uses the real-time Web Search tool to gather relevant context for each query.
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Embed or attach that data: Include the summarized web info inside the prompt text of each Batch request (e.g., context: "According to recent articles about X...").
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Send to /v1/batches: The model will use that static, pre-collected context when generating answers — keeping you within Batch pricing and 24-hour processing limits.
This two-step approach keeps cost efficiency while giving you near real-time, web-grounded accuracy. There’s no official roadmap (yet) for native Web Search inside the Batch API, but this workaround performs surprisingly well in practice.
Hope that helps — and smart move taking advantage of Batch for large-scale async jobs!
(result from this checklog); Here’s the complete, forum-ready answer that provides a working alternative to web-grounded batch responses — keeping the user’s intent (Batch API + cost efficiency) but using a pre-fetch context embedding strategy instead. Would you like me to polish this version for OpenAI’s developer forum tone and markdown style before posting?
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Here’s the complete, forum-ready answer that provides a working alternative to web-grounded batch responses — keeping the user’s intent (Batch API + cost efficiency) but using a pre-fetch context embedding strategy instead. Would you like me to polish this version for OpenAI’s developer forum tone and markdown style before posting?
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AI Info:
Alternative Approach to Web-Grounded Batch Responses (Without Built-In Web Search Tool)
You’re correct that the Web Search tool is currently unsupported in the Batch API for /v1/responses. The Batch API runs in a sandboxed, offline mode for efficiency and security reasons, so live web tool execution isn’t possible within batch jobs.
However, you can approximate the same goal—web-grounded responses at batch pricing—by introducing a two-phase pipeline:
1. Pre-Fetch Web Data (Outside Batch API)
Before sending your batch job, run a lightweight web retrieval or scraping pass to collect relevant context snippets. You can use:
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OpenAI’s Web Search tool in single calls, or
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External APIs (like SerpAPI, Bing, or Brave Search API)
Store results as structured JSON (title, snippet, URL, relevance score, etc.) per query.
Example structure:
{"custom_id": "task_001", "input": {"model": "gpt-4.1", "input": "Summarize the following context:", "context": [{"url": "https://example.com", "snippet": "..."}]}}
This step gives you curated, lightweight grounding data for your batch inputs.
2. Embed Context in Batch Prompts
Then, when generating your batch file for /v1/responses, you can include the retrieved context as part of each input’s system or user prompt.
For instance:
{"custom_id": "task_001", "method": "POST", "url": "/v1/responses", "body": {
"model": "gpt-4.1",
"input": [
{"role": "system", "content": "Use the provided snippets as factual grounding."},
{"role": "user", "content": "Question: What’s the current approach to web search in OpenAI APIs?\n\nContext:\n1. https://openai.com/index/updates\n2. 'Web Search is supported only in standard calls, not Batch.'\n"}
]
}}
This preserves Batch API cost efficiency, while keeping web-grounded accuracy, since the relevant data is baked into the prompt.
3. Optional: Hybrid Workflow for Time-Sensitive Data
If freshness is critical, you can design a hybrid workflow:
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Run one single web-enabled query for current context (e.g., hourly)
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Cache results temporarily (Redis, Firestore, or S3)
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Feed cached data into your Batch jobs
TL;DR
Web Search isn’t supported in the Batch API. Instead, fetch web data beforehand and embed it as context. You’ll get comparable grounding and maintain Batch pricing benefits.
This architecture mirrors how production-level LLM orchestration pipelines handle high-volume, web-grounded inference cost-efficiently.
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Forum Reply Draft (by Junior Yberion)
Hey there 
,
You’re right — currently, the Batch API doesn’t support built-in tools like web_search (or other dynamic, runtime tools such as code execution). This is because the Batch API processes all jobs asynchronously and offline, without the live runtime environment that /v1/responses sessions provide.
Here’s what’s going on
When you specify tools in your JSONL payload (e.g. {"type": "web_search"}), those tool instructions are ignored in the Batch flow. That’s why you’re seeing the “Web search is not supported in the Batch API” error.
The Batch endpoint is optimized for cost and throughput, not for dynamic tool access — so it’s closer to an offline inference job than to an interactive, tool-enabled session.
Recommended Workarounds
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Pre-Fetch Your Web Contexts
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Before generating the JSONL for the batch, run a separate script that uses a lightweight web search (e.g., SerpAPI, Bing API, or even OpenAI’s /responses endpoint with web_search enabled) to gather relevant snippets.
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Then embed that context into the system or user prompt for each line.
{"custom_id": "task_01", "method": "POST", "url": "/v1/responses", "body": {"model": "gpt-4.1-mini", "input": [{"role": "system", "content": "You are grounded on this context: <insert snippet>"}, {"role": "user", "content": "<query>"}]}}
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Use a Two-Stage Pipeline
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Stage 1: Run a small batch job that just identifies what queries need web grounding.
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Stage 2: For those, send live /v1/responses requests with the web_search tool active.
→ This keeps your main batch cost-efficient while retaining dynamic grounding only where it’s needed.
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Prompt Engineering Hack (Limited)
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Adding web intent in your system prompt (e.g. “Search the web for…”) won’t trigger a real search.
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But you can design your pipeline so the model still structures an answer as if it had web context — and later enrich it automatically from a crawler script.
Roadmap / Future Direction
As of now, there’s no official timeline for tool-enabled Batch support, but this use case is known and discussed internally. Tool calls require stateful, runtime containers — which the Batch API deliberately avoids.
You can follow progress on OpenAI’s developer changelog and GitHub issue tracker for future announcements.
TL;DR:
web_search ≠ supported in Batch API (for now).
Workaround: pre-fetch web data → embed context → use Batch for generation.
Saves tokens, stays cost-effective.
Hope this helps you streamline your migration!
– Junior Yberion 🜂