DeepSeek or Gemini Flash: V4.1 Flash vs 3.8 Flash on price and strength

DeepSeek V4.1 Flash and Gemini 3.8 Flash are the two models people most often choose between when they need a cheap API for bulk text tasks. The "deepseek or gemini flash" question usually comes down to two things: how much cheaper one is, and whether the difference in intelligence index is worth the money. Below is a comparison on price, index, caching, and context, with cost math and a script to help you decide on your own data.

Side-by-side table

Prices in USD per 1M tokens, as of 2026-10-06.

The intelligence index is the Artificial Analysis Intelligence Index, a composite benchmark score.

For the current model list and prices, call GET https://api.zukko.pro/v1/models or open the Models page in the dashboard.

DeepSeek or Gemini Flash: what the numbers say

Price. DeepSeek V4.1 Flash is cheaper on both input ($0.013 vs $0.025) and output ($0.038 vs $0.088).

Caching. For DeepSeek, a cache read costs the same as input, so caching changes nothing. For Gemini 3.8 Flash, a cache read ($0.013) is almost half the input price — if the provider reports it in the response — and a write is not charged separately: the first request is billed in full at the input price. With a long repeated prefix, Gemini's input can drop to DeepSeek's price, but not below it.

Hence: at current prices, DeepSeek V4.1 Flash is never more expensive than Gemini 3.8 Flash, whatever the request profile. Every DeepSeek input token costs $0.013 — the same as Gemini's cheapest input option (a cache read) — and DeepSeek's output is cheaper.

A caveat: this comparison assumes the same token count. Models use different tokenizers, and the same text can turn into a different number of tokens. The real cost shows up in usage from a test run on your data.

Intelligence index. 41 for Gemini vs 39 for DeepSeek. A two-point gap in an averaged score doesn't prove Gemini will do better on your specific task. But it isn't zero either, and that's exactly what to test.

Context and images. The same: 1M tokens each, and both are text-only.

Math 1: many short requests

100,000 requests at 800 input and 150 output tokens. That's 80,000,000 input and 15,000,000 output tokens.

Formula: cost = (input × input_price + output × output_price) / 1,000,000.

• DeepSeek V4.1 Flash: 80 × 0.013 + 15 × 0.038 = 1.04 + 0.57 = $1.61
• Gemini 3.8 Flash: 80 × 0.025 + 15 × 0.088 = 2.00 + 1.32 = $3.32

The difference is $1.71 per 100,000 requests.

Math 2: a long shared prefix and caching

10,000 requests, each with the same 8,000-token prefix (instructions and reference text), 500 new tokens, and a 300-token answer. Total input 10,000 × 8,500 = 85,000,000, output 10,000 × 300 = 3,000,000.

DeepSeek V4.1 Flash (cache price equals input price, so caching doesn't matter): 85 × 0.013 + 3 × 0.038 = 1.105 + 0.114 = $1.219

Gemini 3.8 Flash without caching: 85 × 0.025 + 3 × 0.088 = 2.125 + 0.264 = $2.389

Gemini 3.8 Flash if the prefix is read from cache from the second request on:

• first request in full at the input price: 8,500 × 0.025 / 1,000,000 = $0.0002125
• cache read in the other 9,999 requests: 9,999 × 8,000 = 79,992,000 → 79.992 × 0.013 = $1.039896
• fresh input of the rest: 9,999 × 500 = 4,999,500 → 4.9995 × 0.025 = $0.1249875
• output: 3 × 0.088 = $0.264
• total: $1.429096, about $1.429

Even in Gemini's best case, DeepSeek is cheaper by $1.429096 − $1.219 ≈ $0.21. Whether caching kicks in on your traffic shows up in actual charges in the dashboard's Usage section — compare them with the no-cache estimate.

When to pick each model

DeepSeek V4.1 Flash is a sensible default if:

• volume is large and answers are checked by code (labels, JSON, yes/no);
• both models reach about the same accuracy on your examples;
• the budget is tight and counted in millions of tokens.

Gemini 3.8 Flash is worth considering if:

• it gives a clearly better result on your sample, and the quality gain pays for the price gap;
• your requests share a long common prefix, so caching narrows the price gap (see math 2).

There is also Gemini 3.7 Flash (gemini-3.7-flash): $0.013 / $0.05, cache read $0.013. Its price is closer to DeepSeek V4.1 Flash; we have no intelligence index value for it, so include it in your test too if you want to stay with Gemini. Setup and calculations for Gemini 3.8 Flash are in the article Gemini 3.8 Flash via API↗.

How to test on your own data

The fairest method is a blind comparison. The script sends each question to both models, shuffles the answers and saves them as "A" and "B", and writes the model mapping to a separate file. You rate the answers without knowing which model is which, and only then open the key.

import csv
import random
from openai import OpenAI

client = OpenAI(base_url="https://api.zukko.pro/v1", api_key="fetch_your_key")

PRICES = {"deepseek-v4.1-flash": (0.013, 0.038), "gemini-3.8-flash": (0.025, 0.088)}
SYSTEM = "You answer customers of an online store. Be brief and to the point."
spent = {m: 0.0 for m in PRICES}


def ask(model, question):
    resp = client.chat.completions.create(
        model=model,

The workflow:

1. Put 50–100 real requests into questions.txt, one per line, including hard ones.
2. Run the script and fill in the last column of blind.csv.
3. Match your ratings against key.csv and count how often each model won.
4. If results are close, take DeepSeek V4.1 Flash. If Gemini 3.8 Flash is clearly better, multiply the price gap by your monthly volume and decide whether it's worth it.

For tasks with a single correct answer (a label, a number), comparing outputs with a reference in code is enough instead of blind rating. Both requests go through one endpoint and one key — the gateway converts the format. Reference: the chat/completions docs↗ and the model list docs↗.

If you don't have a key yet, create one in the dashboard following the quick start↗ — one key works with all models.

FAQ

Which is cheaper: DeepSeek V4.1 Flash or Gemini 3.8 Flash?

DeepSeek V4.1 Flash: $0.013 / $0.038 vs $0.025 / $0.088 per 1M tokens. At current prices for the same token count it is never more expensive than Gemini 3.8 Flash, whatever the mix of input, output, and cache. The models use different tokenizers, so check actual usage on your data.

Does an index of 41 vs 39 mean Gemini is better?

It means that, on average across a benchmark set, Gemini 3.8 Flash scored slightly higher. On a specific task the result could go either way — test on 50–100 of your own examples.

Do these models understand images?

No, both accept text input only. For images you need a different model, such as DeepSeek V4 Pro (deepseek-v4-pro) or Qwen 3.8 Max (qwen3.8-max).

Can I switch between them without rewriting code?

Yes. Both are called via https://api.zukko.pro/v1 with one key; only the model value changes.