New Sign up free, 10 calls on us. Up to $1, no card needed.

DeepSeek V4.1 Flash vs DeepSeek V4.1 Flash (1017)

vs

Which one, when

These two bill identically, $0.3 per million input, $1.2 per million output and $0.006 per million cached input tokens, and share text and image input, a 1000000-token context and up to 393216 output tokens. The difference is the release: deepseek-v4.1-flash is the 2026-09-10 model and deepseek-v4.1-flash-1017 the 2026-10-17 update, so moving costs nothing extra; keep deepseek-v4.1-flash only where a workload is tuned to the September release.

Benchmarks

DeepSeek V4.1 Flash (1017): the vendor has not published benchmark scores.

Above averageNo peer higherDeepSeek V4.1 Flash14 / 194 / 19
DeepSeek V4.1 Flash DeepSeek V4.1 Flash (1017) other models measured peer average ★ no peer scored higher
DeepSWE 1.1
no peer scored higher 74.2%
N/A
Cybergym
no peer scored higher 88.1%
N/A
GPQA Diamond
90.9%
N/A
Agents' Last Exam
31.8%
N/A
Chartography with tools
78.9%
N/A

Vendor-published: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai

Pricing

DeepSeek V4.1 Flash DeepSeek V4.1 Flash (1017) Δ
Input / 1M tokens $0.3 $0.3 =
Output / 1M tokens $1.2 $1.2 =
Cache read / 1M tokens $0.006 $0.006 =
Cache write no separate charge no separate charge -

Rates from the live catalogue at build time; each model page carries the current rate card.

Where they sit · input price per 1M tokens across all 79 chat models on this billing unit (log scale)

Capabilities

DeepSeek V4.1 Flash DeepSeek V4.1 Flash (1017)
Tool calling yes yes
Thinking control yes (vendor dial not published) yes (vendor dial not published)
Prompt caching implicit (automatic) implicit (automatic)
Cache lifetime no fixed TTL (evicted when unused) no fixed TTL (evicted when unused)
Minimum cached prefix not published not published

Specs

DeepSeek V4.1 Flash DeepSeek V4.1 Flash (1017)
Input modalities text image text image
Output modalities text text
Released 2026-09-10 2026-10-17
Context window 1M 1M
Max output 393K 393K

Specs are transcribed from each vendor’s documentation; a row a vendor does not publish is left out rather than inferred. Full sources: DeepSeek V4.1 Flash · DeepSeek V4.1 Flash (1017)

One prompt, both models, measured through the gateway

PROMPT Explain to a CFO, in exactly three sentences, why our LLM bill doubled after we enabled reasoning. CHECK exactly 3 sentences

DeepSeek V4.1 Flash passed · 3 sentences

Enabling reasoning changed the unit economics: the model now spends extra billable tokens internally to “think” before producing the answer, and those reasoning tokens are charged like output tokens even though users never see them. That added token generation—plus longer contexts and occasional retries on harder prompts—roughly doubled our cost per request. We can control this by capping reasoning depth, routing only complex queries to reasoning models, and caching common prompts to bring the bill back down.

out 423 tok (+327 thinking) latency 14.6 s

DeepSeek V4.1 Flash (1017) passed · 3 sentences

Enabling reasoning caused each request to generate additional internal reasoning tokens, which are billed like output tokens and can be several times longer than the final answer. Those extra tokens raise cost per call, and longer responses plus occasional retries pushed total usage to roughly double. We can control this with reasoning-effort limits, token budgets, caching, and spend monitoring by use case.

out 253 tok (+178 thinking) latency 5.4 s

Instruction following (exactly three sentences, so countable), audience shaping (CFO register), and the hidden-thinking billing gap the token meters below expose.

PROMPT This function is supposed to return the longest run of consecutive equal items, but callers report it is off by one on single-element inputs and misses runs that end at the last index. Fix it and explain the bug in one paragraph. CHECK fix passes tests

DeepSeek V4.1 Flash passed · 8/8 cases

```python def longest_run(xs): if not xs: return 0 best = 1 cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 else: cur = 1 best = max(best, cur) return best ``` The bug is that `cur` is counting equal adjacent pairs, not elements in the current run: every time `xs[i] == xs[i - 1]`, the run length increases by one element, but the first element of the run was never counted, so all runs are undercounted by one. Also, `best` starts at `0`, so single-element inputs return `0` instead of `1`, and a one-element run ending at the last index is missed entirely.

out 918 tok (+733 thinking) latency 11.4 s

DeepSeek V4.1 Flash (1017) passed · 8/8 cases

```python def longest_run(xs): if not xs: return 0 best = cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 else: cur = 1 best = max(best, cur) return best ``` The bug is that `cur` starts at `0` and is incremented only when two adjacent items match, so it counts equal adjacent pairs rather than run length; a run of length `k` is recorded as `k - 1`. Consequently, a single-element input returns `0`, and a run ending at the last index—especially a one-element final run—is undercounted or never counted. The fix initializes every run to length `1`, increments on a match, resets to `1` on a mismatch, updates `best` each iteration, and handles the empty input separately.

out 1300 tok (+1095 thinking) latency 11.8 s

Whether the fix is actually correct (runnable), explanation density, and token efficiency on a bounded task.

PROMPT Extract a JSON object with fields {company, quarter, revenue_usd_m, growth_pct, guidance} from this text. Use null for anything not stated; add no other fields. CHECK valid JSON, schema exact

DeepSeek V4.1 Flash passed · 5/5 fields, guidance "withheld pending the Q4 close"

{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11.0,"guidance":"withheld pending the Q4 close"}

out 1707 tok (+1667 thinking) latency 15.1 s

DeepSeek V4.1 Flash (1017) passed · 5/5 fields, guidance "withheld pending the Q4 close"

{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11,"guidance":"withheld pending the Q4 close"}

out 1375 tok (+1337 thinking) latency 12.2 s

Schema obedience (no invented fields), hallucination pressure (guidance is explicitly withheld), and structured-output path differences.

PROMPT Write a 120-word product announcement for an API gateway feature that caches prompts across providers. Forbidden words: "seamless", "unlock", "game-changer", "revolutionize", "empower". Exactly one sentence must be a question. CHECK 120 words, 0 banned words

DeepSeek V4.1 Flash passed · 116 words, 0 banned, 1 question

Today we're launching Cross-Provider Prompt Cache for our API gateway. It stores identical prompt requests and their responses across supported model providers, then serves cached results when a match is found. Teams can cut duplicate inference costs, reduce latency, and keep behavior consistent during provider failover. The cache works with configurable TTLs, per-route rules, and cache-key controls, so you decide what is reusable and what must stay fresh. Does your application send the same prompts to multiple providers? Now your gateway can answer many of those calls without another upstream request. Existing observability dashboards show hit rates, saved tokens, and estimated spend reduction. Enable it in the gateway console, set your policy, and start caching today.

out 917 tok (+770 thinking) latency 9.0 s

DeepSeek V4.1 Flash (1017) passed · 120 words, 0 banned, 1 question

Today we’re introducing Cross-Provider Prompt Cache in the API gateway. It stores identical prompt requests once and serves cached responses across supported model providers, reducing duplicate calls, latency, and spend. Teams can set cache keys by model, temperature, system prompt, and custom metadata, then choose TTLs per route or workspace. The gateway handles provider fallback while keeping cached results consistent with your policy. Is your team paying twice for the same prompt? Cross-Provider Prompt Cache helps you answer that. Configure it in the gateway dashboard, inspect hit rates by provider, and invalidate entries on demand. Existing gateway routes need no code changes; enable the feature and define your caching rules. Cross-Provider Prompt Cache is available for all API gateway customers.

out 2487 tok (+2335 thinking) latency 17.1 s

Constraint obedience (word budget, banned-word list, the single question), style fingerprint, and length control.

Switch between them with one line

Both ids are in every tab below; the highlighted pair of lines is the only edit. Same endpoint, same key, same request shape.

from openai import OpenAI

client = OpenAI(
    base_url="https://synthorai.io/v1",
    api_key="sk-syn-...",
)

resp = client.chat.completions.create(
    model="deepseek-v4.1-flash",
    # model="deepseek-v4.1-flash-1017",  # uncomment this line, comment the one above
    messages=[{"role": "user", "content": "Summarize this diff"}],
    reasoning_effort="medium",
)
print(resp.choices[0].message.content)

Get your API key →

FAQ

Which is cheaper, DeepSeek V4.1 Flash or DeepSeek V4.1 Flash (1017)?

They list the same price on the "Input / 1M tokens" row ($0.3), so price does not decide this one. See the specs and capabilities below.

Can I A/B test DeepSeek V4.1 Flash against DeepSeek V4.1 Flash (1017) without two integrations?

Yes. Both are served through the same OpenAI-compatible endpoint with one API key. Switching is a one-line change to the model id, so you can route a fraction of traffic to each and compare bills directly.

Do DeepSeek V4.1 Flash and DeepSeek V4.1 Flash (1017) support prompt caching?

Yes. Both bill cache reads below their input rate, so warm-prefix workloads cost less than the list rates suggest. The exact cache-read rows are in the pricing table above.

Related comparisons

From our measured studies