DeepSeek Comparison Tool: Match the Right Model to Your Workload

You opened a DeepSeek comparison tool because you have a real decision to make: should this workload run on deepseek-flash or on deepseek-v4-pro? As of 22 October 2026 they are the two models on DeepSeek’s official pricing page, served by DeepSeek-V4.1-Flash-1017 and DeepSeek-V4-Pro-0813 respectively. For the same token counts, Pro’s token charges are 3.3× to 7.33× Flash’s, depending on the mix of cache hits, cache misses and output (about 3.6× in the worked example below), and only Flash accepts images. This guide explains what the tool shows and how to compare the two models with each other and with named GPT and Claude models, using DeepSeek’s official pricing page and its dated, vendor-reported Change Log figures.

What a DeepSeek comparison tool actually needs to compare

The tool above has three parts. The workload-based suggestion covers nine use cases (chat, single-file code, multi-file or agentic coding, math and reasoning, RAG, tool-calling agents, batch jobs, vision, simple function calling) and returns a suggested model, a short rationale and its peak and off-peak rate card. It suggests deepseek-flash in all nine; vision works only on Flash, and deepseek-v4-pro is an option to compare against, not a default. Several rationales cite DeepSeek’s 10 September 2026 statement that V4.1-Flash benchmarks ahead of V4-Pro, labelled as a vendor claim. The official spec sheet matches the table below, and the vendor-reported benchmarks panel shows DeepSeek’s own dated figures. A footnote dates the last successful daily fetch from DeepSeek’s pricing page; a failed fetch keeps the last good prices.

The legacy names deepseek-v4-flash and deepseek-v4-flash-vision-exp are still accepted but retired: their requests are served by DeepSeek-V4.1-Flash-1017 at the Flash price. deepseek-chat and deepseek-reasoner were retired on 24 July 2026.

The current generation at a glance

Both models share a 1M-token context, 384K-token maximum output, thinking and non-thinking modes and most API features; vision (Flash only), the Responses API web_search tool (Pro only), concurrency and price differ. The V4 line began with the V4 Preview on 24 April 2026, when DeepSeek listed V4-Pro at 1.6T total / 49B active parameters and V4-Flash at 284B / 13B. DeepSeek-V4-Pro-0813 reached GA on 13 August 2026. DeepSeek’s 10 September 2026 note described V4.1-Flash, that month’s build, as a 552B-parameter MoE with 8B active parameters for input and 16B for output. DeepSeek-V4.1-Flash-1017 followed on 17 October 2026.

Side-by-side: deepseek-flash vs deepseek-v4-pro (as of 22 October 2026)

This table mirrors the tool’s spec sheet, from DeepSeek’s official pricing page as of 22 October 2026 (USD per 1M tokens, peak / off-peak).

Off-peak rates are half the peak rates (peak windows: see the gotchas). Pro costs 7.33× as much as Flash on cache-hit input ($0.044 ÷ $0.006), 4.4× on cache-miss input ($1.32 ÷ $0.30) and 3.3× on output ($3.96 ÷ $1.20), in both periods.

Code still sending deepseek-chat or deepseek-reasoner, which DeepSeek retired at 15:59 UTC on 24 July 2026, must switch to deepseek-flash or deepseek-v4-pro; the base_url stays https://api.deepseek.com. Thinking is now the default, so a former deepseek-chat call that should stay non-thinking must send "thinking": {"type": "disabled"} (OpenAI Python SDK: extra_body={"thinking": {"type": "disabled"}}).

How to compare benchmarks honestly

Benchmark tables are where comparison tools mislead readers: versions drift and harnesses differ. Name the benchmark version, the model build and the date, and label vendor numbers as such. The tool’s benchmark panel lists DeepSeek’s own figures as published, with a link to the API Change Log, for V4.1-Flash-1017 (17 October 2026) and V4-Pro-0813 GA (13 August 2026). It marks them as vendor-reported, not independent, and warns that versions and harnesses differ, so most rows are not head-to-head.

Coding

DeepSeek reported DeepSWE v1.1 77.0 (Mini-SWE-V2 framework), Terminal-Bench 4.0 42.3 (Claude Code framework) and NL2Repo-Bench 68.2 for V4.1-Flash-1017 (17 October 2026), and Terminal Bench 2.1 87.9, DeepSWE 62.7 and NL2Repo 61.5 for V4-Pro GA (13 August 2026; DeepSeek Harness minimal mode, per its model card). DeepSeek’s April 2026 V4 Preview materials reported SWE-Bench Verified 80.6% for V4-Pro (Preview) at max effort, a Preview figure that does not describe V4-Pro-0813.

Reasoning, math and knowledge

Both lists include HLE without tools: 42.6 for V4.1-Flash-1017 (17 October 2026; 44.0 on the pure-text subset) and 42.7 for V4-Pro GA (13 August 2026; 60.0 with tools). The notes are two months apart, so the pair is only indicative. DeepSeek’s 10 September 2026 note claims V4.1-Flash benchmarks ahead of flagship models including V4-Pro, a vendor claim. DeepSeek’s 24 April 2026 note said V4-Pro (Preview) led all open models on world knowledge, trailing only Gemini-3.1-Pro.

Independent indices and speed

We quote no third-party indices or speed figures, and the tool shows none. An aggregator score published before 13 August 2026 cannot describe V4-Pro-0813, nor can one from before 17 October 2026 describe V4.1-Flash-1017. Measure speed on your own workload.

Thinking modes: a parameter, not a model

Thinking is a request setting, not a separate model ID; until July 2026 the legacy deepseek-reasoner name selected it. Two request fields control it, per DeepSeek’s Thinking Mode guide:

• Toggle — "thinking": {"type": "enabled"} (the default) or {"type": "disabled"}; with the OpenAI Python SDK it goes in extra_body.
• Effort — reasoning_effort: low, high (the default) or max, three levels since the 13 August 2026 Change Log entry.
• Non-thinking (toggle disabled) — usually fewer output tokens; FIM completion (beta) works only in this mode.

In thinking mode, temperature, presence_penalty and frequency_penalty have no effect; the chain of thought returns in reasoning_content and counts as output tokens. Requests go to POST https://api.deepseek.com/chat/completions:

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.deepseek.com",
    api_key=os.environ.get("DEEPSEEK_API_KEY"),
)

resp = client.chat.completions.create(
    model="deepseek-flash",  # or "deepseek-v4-pro"
    messages=[{"role": "user", "content": "Plan the migration."}],
    reasoning_effort="high",
    extra_body={"thinking": {"type": "enabled"}},
)

print(resp.choices[0].message.reasoning_content)
print(resp.choices[0].message.content)

The Anthropic-format endpoint is https://api.deepseek.com/anthropic (set ANTHROPIC_BASE_URL to it), with the same two model names.

Worked cost comparison: same job, two models, peak and off-peak

This illustrative, hypothetical workload is 1,000,000 calls, each with a 2,000-token system prompt (assumed to be a cache hit every time), a 200-token user message (cache miss) and a 300-token response that includes any reasoning tokens: 2,000,000,000 cache-hit, 200,000,000 cache-miss and 300,000,000 output tokens. Each line is tokens ÷ 1,000,000 × rate at the 22 October 2026 prices; the results are exact to the cent.

deepseek-flash

• Cache-hit input: 2,000,000,000 ÷ 1,000,000 × $0.006 = $12.00 peak; × $0.003 = $6.00 off-peak
• Cache-miss input: 200,000,000 ÷ 1,000,000 × $0.30 = $60.00 peak; × $0.15 = $30.00 off-peak
• Output: 300,000,000 ÷ 1,000,000 × $1.20 = $360.00 peak; × $0.60 = $180.00 off-peak
• Total: $432.00 peak / $216.00 off-peak

deepseek-v4-pro

• Cache-hit input: 2,000,000,000 ÷ 1,000,000 × $0.044 = $88.00 peak; × $0.022 = $44.00 off-peak
• Cache-miss input: 200,000,000 ÷ 1,000,000 × $1.32 = $264.00 peak; × $0.66 = $132.00 off-peak
• Output: 300,000,000 ÷ 1,000,000 × $3.96 = $1,188.00 peak; × $1.98 = $594.00 off-peak
• Total: $1,540.00 peak / $770.00 off-peak

Pro costs about 3.6× as much on this workload (1,540 ÷ 432 = 3.5648…, the same off-peak; rounded to one decimal), and output makes up most of both bills.

For context, as of 22 October 2026 OpenAI’s pricing page lists GPT-5.4 at $2.50 input / $15.00 output per 1M tokens (standard tier, prompts up to 272K tokens), and Anthropic’s pricing page lists Claude Opus 4.6, now a legacy model, at $5 / $25. GPT-5.4 is not in OpenAI’s featured line-up either. At peak, Flash’s cache-miss input and output rates are 88% and 92% below GPT-5.4’s (1 − 0.30 ÷ 2.50; 1 − 1.20 ÷ 15.00), and Pro’s are 73.6% and 84.2% below Claude Opus 4.6’s (1 − 1.32 ÷ 5.00; 1 − 3.96 ÷ 25.00); off-peak, halving DeepSeek’s rates in those calculations gives 94% and 96%, and 86.8% and 92.1% (all rounded to one decimal). Per-token prices say nothing about quality, and tokenization differs between models.

Decision framework: which model when

Use this checklist: editorial guidance built from the published facts above, not from deployments or evaluations of our own.

1. Default to deepseek-flash (DeepSeek-V4.1-Flash-1017). It is cheaper in every bucket, the only one of the two that accepts images, and allows 2,500 concurrent requests to Pro’s 500. DeepSeek says V4.1-Flash benchmarks ahead of V4-Pro; that is a vendor claim, so evaluate it on your own tasks first.
2. Consider deepseek-v4-pro (DeepSeek-V4-Pro-0813) to compare on your own workload or to keep a workflow already validated on it. It costs 3.3× (output) to 7.33× (cache-hit input) as much and does not accept images; DeepSeek dropped a plan to route it to V4.1-Flash from 14 September 2026 (Change Log).
3. Tune thinking rather than switching it on. It is on by default at effort high. Use max for hard math and planning; for short replies, use low or "thinking": {"type": "disabled"} to cut output tokens.
4. Use deepseek-flash in new code. For the legacy names, see the migration note above and the FAQ.
5. Compare against named GPT and Claude models with their pricing pages open. Competitor prices here were checked on 22 October 2026.

Comparison-tool gotchas that quietly break your math

• Mixing models in one cost calc. Price each call with one model; blend peak and off-peak only by your real share of peak-hour traffic.
• Forgetting the uncached input bucket. A cached system prompt does not cover each new user message. Use three buckets × two periods (cache hit, cache miss, output; peak, off-peak).
• Ignoring the peak/off-peak split. Off-peak has been half the peak rate since 16:00 UTC on 16 August 2026. Peak hours are 01:00–04:00 and 06:00–10:00 UTC, Monday to Friday, excluding Chinese public holidays: 35 of the 168 hours in a normal week (≈20.8%). An earlier off-peak discount ended on 5 September 2025; that is history.
• Quoting an old rate as current. Treat rates older than the 22 October 2026 peak / off-peak sheet as history; prices can change.
• Hitting the concurrency ceiling. Per account, deepseek-flash allows 2,500 concurrent requests and deepseek-v4-pro 500; above that, the API returns HTTP 429.

Next, the DeepSeek pricing calculator prices one workload at peak and off-peak for either model or both side by side, the DeepSeek cost estimator scales two scenarios to per-day, per-month and per-year costs, and the DeepSeek benchmarks 2026 page collects published benchmark figures.

DeepSeek AI Guide is an independent resource and is not affiliated with DeepSeek or its parent company. Model IDs, pricing and API behaviour change; check the official DeepSeek pricing page and the DeepSeek API Change Log before committing to a production decision. The DeepSeek-V4 technical report covers the April 2026 V4 Preview.