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

DeepSeek V4.1 Flash vs Gemini 3.6 Flash

vs

Which one, when

deepseek-v4.1-flash undercuts gemini-3.6-flash across the board on text: $0.3 vs $1.5 input (5x), $1.2 vs $7.5 output (6.25x), and $0.03 vs $0.15 cached input, with a similar 1000000-token window plus a 393216-token max output that is six times gemini-3.6-flash's 65536; pick it for long generations and cheap bulk text and image work. Choose gemini-3.6-flash when the input itself is video or audio, which deepseek-v4.1-flash cannot accept; audio there is priced separately at $5 per million audio tokens, and its thinking cannot be turned off.

Benchmarks

Above averageNo peer higherDeepSeek V4.1 Flash16 / 194 / 19Gemini 3.6 Flash5 / 191 / 19
DeepSeek V4.1 Flash Gemini 3.6 Flash other models measured peer average no peer scored higher
Terminal-Bench 2.1
no peer scored higher 90.6%
78%
BioMysteryBench hard
N/A
41.2%
OSWorld 2.0
N/A
33.8%
Cybergym
no peer scored higher 88.1%
N/A
GDPval-AA v2 Elo · 1422-1598 per Google · 2026-08-25
N/A
1422
Harvey Lab-AA
N/A
85.1%
GPQA Diamond
90.9%
N/A
Agents' Last Exam
31.8%
N/A
LVBench
N/A
84.2%

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

Pricing

DeepSeek V4.1 Flash Gemini 3.6 Flash Δ
Input / 1M tokens $0.3 $1.5 0.2×
Output / 1M tokens $1.2 $7.5 0.16×
Cache read / 1M tokens $0.03 $0.15 0.2×
Cache write no separate charge - -

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

Where they sit - input price per 1M tokens across all 69 chat models on this billing unit (log scale)

Capabilities

DeepSeek V4.1 Flash Gemini 3.6 Flash
Tool use yes yes
Thinking control yes - vendor dial not published always on
Structured output - yes
Prompt caching implicit (automatic) implicit + explicit
Cache lifetime no fixed TTL (evicted when unused) not published
Minimum cached prefix not published 4096 tokens

Specs

DeepSeek V4.1 Flash Gemini 3.6 Flash
Input modalities text image text image audio video
Output modalities text text
Released 2026-09-10 2026-07-21
Knowledge cutoff - 2026-03
Context window 1M 1M
Max output 393K 66K
Thinking parameter - thinkingLevel
Accepted values -
  • minimal
  • low
  • medium
  • high
Default - medium

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 · Gemini 3.6 Flash

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

Gemini 3.6 Flash passed · 3 sentences

Enabling reasoning causes the model to generate thousands of internal "thinking" tokens behind the scenes to process logic before delivering the final response. Because our vendor bills us for every token generated—including these intermediate processing steps—our billable output volume per query surged significantly. Since output tokens carry a much higher unit cost than input tokens, this jump in processing volume directly doubled our overall spend.

out 893 tok (+816 thinking) latency 4.7 s

Instruction following (exactly three sentences - 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

Gemini 3.6 Flash passed · 8/8 cases

Here is the fixed function along with an explanation of the bugs: ```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 ``` ### Explanation The original function initialized `cur` and `best` to `0` and incremented `cur` on matching adjacent items, which meant it was counting identical adjacent *pairs* rather than the total number of *elements* in a run. This caused single-element inputs to skip the loop and return `0` instead of `1`, and undercounted all run lengths by 1 (for example, a run of two items at the end of the list resulted in `cur = 1`). […]

out 2034 tok (+1768 thinking) latency 10.0 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

Gemini 3.6 Flash passed · 5/5 fields, guidance "withheld"

```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": null, "guidance": "withheld" } ```

out 2843 tok (+2783 thinking) latency 13.1 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

Gemini 3.6 Flash missed · 60 words, 0 banned, 0 questions

72: reducing 73: your 74: monthly 75: token 76: spend. S6 (21): 77: You 78: can 79: easily 80: set 81: custom 82: expiration 83: rules, 84: configure 85: TTL 86: settings, 87: and 88: manage 89: cache 90: invalidation 91: across 92: all 93: vendors 94: from 95: one 96: centralized 97: dashboard. S7 (23): 98: Start 99: optimizing […]

out 4092 tok (+3929 thinking) latency 16.3 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="gemini-3.6-flash",  # 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 Gemini 3.6 Flash?

DeepSeek V4.1 Flash is cheaper on input / 1m tokens ($0.3 vs $1.5, 5.0× apart). Other rows may point the other way - the table above carries the full card, and real cost depends on your mix.

Can I A/B test DeepSeek V4.1 Flash against Gemini 3.6 Flash without two integrations?

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

Do DeepSeek V4.1 Flash and Gemini 3.6 Flash support prompt caching?

Yes - both bill cached 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