# DeepSeek V4.1-Flash-1017 vs DeepSeek V3: Which to Choose for API and Apps > AI Release · @ai_release1 · https://ai-release.org/guides/deepseek-v4-1-flash-1017-ili-deepseek-v3-chto-vybrat-dl_en.html ## TL;DR - **Time to complete:** about 45 minutes to build the test harness, plus however long your 20 test prompts take to run against both models. - **Difficulty:** Intermediate — you need Python and an API key, no GPU. - **You will need:** Python 3.10+, `pip`, the `openai` package (any recent 1.x), an API key for the provider that serves both models, the exact model ID strings for **DeepSeek V3** and **DeepSeek V4.1-Flash-1017**, and a CSV of 20 real prompts from your own product. - **Result:** a `results.csv` with latency, token counts and cost per model, a `routes.json` that sends each task type to the winning model, and a verified routing function. - **Not quoted here on purpose:** prices, context windows, rate limits and benchmark scores. These change between releases, so read them from the provider's model card and pricing page on the day you run Step 1. Wherever a number is missing below, the text says **check the documentation**. ## Requirements and setup ![Illustration: Requirements and setup](https://ai-release.org/guides/img/deepseek-v4-1-flash-1017-ili-deepseek-v3-chto-vybrat-dl-en-1.jpg) - **OS:** Linux, macOS, or Windows 10/11 with WSL2. All commands below are bash. - **Python:** `python3 --version` must print `3.10` or newer. - **SDK:** `pip install --upgrade openai`. This is an OpenAI-compatible client — a library that speaks the `/chat/completions` request format, which is the format both DeepSeek models are called with. - **API key:** store it in the environment variable `DEEPSEEK_API_KEY`, never in the source file. - **Endpoint:** the base URL of your provider's OpenAI-compatible API, for example `https:///v1`. Replace `` with the host printed in your provider console. - **Model IDs:** two shell variables, `MODEL_V3` and `MODEL_FLASH`, filled with the exact ID strings in Step 1. - **Test data:** `prompts.csv` with the header `id,task,prompt`, 20 rows taken from real traffic. - **Permissions:** the right to create API keys and read usage/billing in the provider console. - **Disk:** a few MB. Inference runs on the provider's side over HTTPS. **Example:** a valid `prompts.csv` start. ```csv id,task,prompt 1,extract,"Return JSON with the invoice number and total from: Invoice 4471, total 128.40 EUR, due 2025-03-14" 2,summary,"Summarise in one sentence: the customer says the export button returns a 500 error since this morning" 3,chat_short,"Reply to: can I change the delivery address after the order shipped?" ``` **Why the `task` column matters:** you are not choosing one model for everything. You are collecting per-task evidence so Step 3 can route `extract` and `chat_short` differently. For a wider view of how model families differ on assistant-style tasks, see [ChatGPT, Claude, Gemini, DeepSeek: AI assistant comparison](https://ai-release.org/guides/sravnenie-ii-assistentov.html?utm_source=tg&utm_medium=channel&utm_campaign=guide_inline&utm_content=guide_to_guide). ## Step 1. Pin the exact model IDs and confirm both are reachable **What to do:** export the key and base URL, ask the API which models your key can see, and copy the IDs verbatim. ```bash export DEEPSEEK_API_KEY="sk-" export DEEPSEEK_BASE_URL="https:///v1" curl -s "$DEEPSEEK_BASE_URL/models" \ -H "Authorization: Bearer $DEEPSEEK_API_KEY" | python3 -m json.tool ``` **Why:** `DeepSeek V3` and `DeepSeek V4.1-Flash-1017` are product names; the API accepts opaque ID strings. A guessed string fails at request time with a 404, and you would waste an hour blaming the network. **How to check:** the response is JSON of the shape `"data": [ { "id": "..." }, ... ]`. Filter just the IDs: ```bash curl -s "$DEEPSEEK_BASE_URL/models" \ -H "Authorization: Bearer $DEEPSEEK_API_KEY" | grep -o '"id": *"[^"]*"' ``` You should see one line containing the V3 family ID and one containing the V4.1-Flash-1017 family ID. Then pin them: ```bash export MODEL_V3="" export MODEL_FLASH="" echo "$MODEL_V3 / $MODEL_FLASH" ``` The `echo` must print both strings, not the empty placeholder. Finally, record the fields below in a note file — you will need them in Step 2 for the cost calculation. | Field to compare | DeepSeek V3 | DeepSeek V4.1-Flash-1017 | Where to read it | |---|---|---|---| | Exact model ID | `` | `` | `/models` response | | Context window (tokens) | check the documentation | check the documentation | model card | | Price per 1M input tokens | check the documentation | check the documentation | pricing page | | Price per 1M output tokens | check the documentation | check the documentation | pricing page | | Rate limit (requests/min) | check the documentation | check the documentation | console limits page | | Streaming / tool calling support | check the documentation | check the documentation | model card | **Why a table and not a web search:** pricing pages get cached and quoted out of date; the console value for your own account is the one that shows up on your invoice. ## Step 2. Run the same 20 prompts against both models and measure ![Illustration: Step 2. Run the same 20 prompts against both models and measure](https://ai-release.org/guides/img/deepseek-v4-1-flash-1017-ili-deepseek-v3-chto-vybrat-dl-en-2.jpg) **What to do:** create `ab_test.py`, set the four price variables from Step 1, and run it. The script loops over every prompt twice — once per model — with `temperature=0` so repeated runs are comparable. ```python import csv, os, time from openai import OpenAI client = OpenAI(api_key=os.environ["DEEPSEEK_API_KEY"], base_url=os.environ["DEEPSEEK_BASE_URL"]) MODELS = {"v3": os.environ["MODEL_V3"], "flash": os.environ["MODEL_FLASH"]} PRICE = {"v3": (float(os.environ["PRICE_IN_V3"]), float(os.environ["PRICE_OUT_V3"])), "flash": (float(os.environ["PRICE_IN_FLASH"]), float(os.environ["PRICE_OUT_FLASH"]))} with open("prompts.csv", newline="", encoding="utf-8") as f, \ open("results.csv", "w", newline="", encoding="utf-8") as out: w = csv.writer(out) w.writerow(["prompt_id", "task", "model", "latency_ms", "in_tokens", "out_tokens", "cost_usd", "answer"]) for p in csv.DictReader(f): for key, model_id in MODELS.items(): t0 = time.perf_counter() try: r = client.chat.completions.create( model=model_id, messages=[{"role": "user", "content": p["prompt"]}], temperature=0, ) ms = (time.perf_counter() - t0) * 1000 u = r.usage pin, pout = PRICE[key] cost = u.prompt_tokens / 1e6 * pin + u.completion_tokens / 1e6 * pout ans = (r.choices[0].message.content or "").replace("\n", " ")[:200] row = [u.prompt_tokens, u.completion_tokens, round(cost, 6), ans] except Exception as e: ms = (time.perf_counter() - t0) * 1000 row = ["", "", 0, repr(e)[:200]] w.writerow([p["id"], p["task"], key, round(ms)] + row) ``` ```bash export PRICE_IN_V3="" PRICE_OUT_V3="" export PRICE_IN_FLASH="" PRICE_OUT_FLASH="" python3 ab_test.py ``` **Why `latency_ms` is measured client-side:** it includes network round-trip and queueing, which is exactly what your users feel. Provider-side token counters tell you nothing about the wait. **How to check:** line count and error count. ```bash wc -l results.csv # 20 prompts x 2 models + 1 header = 41 grep -c "Error code" results.csv # must print 0 head -n 3 results.csv ``` The first two data rows must share the same `prompt_id` and differ only in `model` (`v3`, then `flash`). Then aggregate per model — median, not mean, so one slow call does not skew the result: ```bash python3 - <<'PY' import csv, statistics from collections import defaultdict d = defaultdict(list) for r in csv.DictReader(open("results.csv")): d[r["model"]].append(r) for m, rows in d.items(): lat = [int(r["latency_ms"]) for r in rows] cost = [float(r["cost_usd"]) for r in rows] print(m, "calls:", len(rows), "p50_ms:", int(statistics.median(lat)), "total_usd:", round(sum(cost), 4)) PY ``` Expected output — two lines, one per model: ``` v3 calls: 20 p50_ms: total_usd: flash calls: 20 p50_ms: total_usd: ``` **Example:** if `v3` prints `p50_ms: 2400` and `flash` prints `p50_ms: 900` on your prompt set, Flash wins on latency for those 20 prompts — but only for those prompts, and only on that day. For a worked example of how to read such a comparison without over-interpreting it, see [GPT-6.1 Sol vs GPT-4o: Model Comparison and Which to Choose](https://ai-release.org/guides/gpt-6-1-sol-ili-gpt-4o-sravnenie-modeley-i-chto-vybrat.html?utm_source=tg&utm_medium=channel&utm_campaign=guide_inline&utm_content=guide_to_guide). ## Step 3. Score quality, write the routing config, and verify it ![Illustration: Step 3. Score quality, write the routing config, and verify it](https://ai-release.org/guides/img/deepseek-v4-1-flash-1017-ili-deepseek-v3-chto-vybrat-dl-en-3.jpg) **What to do:** add a manual score column, compute the win rate, then encode the decision in `routes.json` and test the loader. Create `scores.csv` by reading the `answer` column for each row and giving 1–5 points against your own acceptance criterion (for `extract`: correct JSON and correct numbers, otherwise 1). ```csv prompt_id,model,score_1_5 1,v3,5 1,flash,3 2,v3,4 2,flash,4 ``` **Why:** latency alone is misleading. A model that answers in 900 ms but returns a wrong invoice total costs more than a 2.4 s call, because a human has to re-check it. | If your `results.csv` and `scores.csv` show... | Then route | |---|---| | One model scores clearly higher on a task | send that task to it | | Scores within 1 point and p50 latency differs a lot | send the high-volume short task to the faster model | | Scores within 1 point and latency is close | compare `total_usd` per 1k requests and take the cheaper | | One model produces most of the `Error code 429` rows | keep the other as `default`, use the throttled one only as fallback | | A task fails on both models | do not route around it — fix the prompt or the input schema first | Encode the outcome: ```json { "default": "v3", "routes": { "extract": "v3", "summary": "flash", "chat_short": "flash" } } ``` ```python import json ROUTES = json.load(open("routes.json", encoding="utf-8")) def pick_model(task: str) -> str: return ROUTES["routes"].get(task, ROUTES["default"]) print(pick_model("summary"), pick_model("unknown_task")) ``` Expected screen output: `flash v3`. The `.get(...)` fallback is why an unexpected task name never crashes the request path. **How to check:** send five real requests per route through the function in staging and confirm the model ID written to your logs matches the table above. ```bash grep -o '"model": *"[^"]*"' app.log | sort | uniq -c ``` Success looks like: the counts for `summary` and `chat_short` land on the Flash ID, `extract` lands on the V3 ID, and no `Error code` lines appear in the tail of the log. ## Troubleshooting **Problem. Every request fails with a model error, but the key works in `/models`.** - Error: `Error code: 404 - {'error': {'message': 'Model Not Exist'}}` - Cause: the model ID was typed from memory or copied with a trailing space, so the string does not match the catalogue. Marketing names are not IDs. - Solution: re-read the ID from the API and re-export it verbatim: ```bash curl -s "$DEEPSEEK_BASE_URL/models" -H "Authorization: Bearer $DEEPSEEK_API_KEY" \ | grep -o '"id": *"[^"]*"' export MODEL_FLASH="" python3 -c "import os; print(repr(os.environ['MODEL_FLASH']))" ``` The `repr` output must show no trailing space inside the quotes. Exact wording of the error text depends on the provider and SDK version — check the documentation for the current message. **Problem. The key works in the browser console but the script returns 401.** - Error: `Error code: 401 - {'error': {'message': 'Authentication Fails'}}` - Cause: the variable was exported in a different shell session, or the key was pasted with surrounding quotes so the header carries `Bearer "sk-..."`. - Solution: verify the variable is visible to the process that runs Python, then re-export without quotes: ```bash echo "${DEEPSEEK_API_KEY:0:6}...${#DEEPSEEK_API_KEY}" export DEEPSEEK_API_KEY=sk- ``` The first command prints the prefix and the length; a length far above the real key length means stray characters. **Problem. The run stops halfway with rate-limit errors, or p50 latency jumps after the first few calls.** - Error: `Error code: 429 - {'error': {'message': 'Rate limit reached'}}` - Cause: the loop fires 40 requests back to back with no delay, exceeding the requests-per-minute limit you recorded in Step 1. - Solution: add a retry with backoff and a small pause between calls: ```python import time for attempt in range(4): try: r = client.chat.completions.create(model=model_id, messages=[{"role": "user", "content": p["prompt"]}], temperature=0) break except Exception as e: if "429" not in repr(e) or attempt == 3: raise time.sleep(2 ** attempt) ``` Re-run and confirm `grep -c "Error code" results.csv` prints `0` again. ## Summary and next steps You now have measured per-task evidence for DeepSeek V3 and DeepSeek V4.1-Flash-1017 on your own prompts, plus a `routes.json` that sends each task to the model your data supports. Next, wire `pick_model()` into your request handler, log the resolved model ID on every call, and re-run `ab_test.py` whenever either model's ID, price or rate limit changes. ## FAQ **Is DeepSeek V4.1-Flash-1017 better than DeepSeek V3?** Not in general — only on your prompts. This is why Step 2 runs both models on the same 20 inputs and Step 3 scores the answers; a "better" label that is not tied to a task, a prompt set and a date is not actionable. **How do I get the exact model ID strings?** Call the `/models` endpoint with your key, as shown in Step 1, and copy the `id` values verbatim into `MODEL_V3` and `MODEL_FLASH`. Never retype them from a blog post. **Where do I find the prices for the cost column?** On the provider's pricing page for your account — the same values you put into `PRICE_IN_V3`, `PRICE_OUT_V3`, `PRICE_IN_FLASH` and `PRICE_OUT_FLASH`. If a value is not published for a field, check the documentation rather than estimating. **Do I have to use both models in production?** No. Many teams end with `routes.json` pointing almost everything at one model and keeping the second only as a fallback for 429 and timeout errors. The value of the harness is the evidence behind that single choice.