You opened a DeepSeek comparison tool because you have a real decision to make: do you ship `deepseek-v4-pro` and pay frontier-tier rates, fall back to `deepseek-v4-flash` for the cost savings, or keep your existing `deepseek-chat` integration running until the legacy IDs retire? The wrong call can multiply a monthly bill by seven, or quietly cap quality on a workload that needs more reasoning headroom. This guide walks through how to compare DeepSeek’s current models against each other and against the GPT-5 and Claude 4 families, using the numbers DeepSeek published on April 24, 2026 alongside third-party verification. By the end you will have a repeatable framework — pricing, context, benchmarks, thinking modes — to pick a model with confidence.

What a DeepSeek comparison tool actually needs to compare

A useful comparison tool is not a feature checklist. It is a decision aid that surfaces the four variables that change your bill or your output quality: the model tier, the thinking-mode setting, the token rates across cache-hit, cache-miss and output buckets, and the benchmark relevant to your workload. Anything else is decoration.

The current DeepSeek line-up is shorter than it used to be. The official model list returns just two IDs: deepseek-v4-flash and deepseek-v4-pro. Legacy aliases still resolve, but the migration window is closing.

The current generation at a glance

DeepSeek V4 launched on April 24, 2026 as a preview release. DeepSeek dropped the first of its V4 series as two preview models, DeepSeek-V4-Pro and DeepSeek-V4-Flash. Both models are 1 million token context Mixture of Experts. Pro is 1.6T total parameters, 49B active. Flash is 284B total, 13B active. They use the standard MIT license. Both are open-weight; they support OpenAI ChatCompletions and Anthropic APIs, with both models supporting 1M context and dual modes (Thinking / Non-Thinking).

Side-by-side: DeepSeek V4-Pro vs V4-Flash vs legacy IDs

This is the table to start every comparison from. Pricing is sourced from DeepSeek’s official pricing page and OpenRouter listings as of April 25, 2026.

DeepSeek’s pricing page states the model names deepseek-chat and deepseek-reasoner will be deprecated, and they correspond to the non-thinking mode and thinking mode of deepseek-v4-flash respectively. If you maintain an older integration, the migration is a one-line `model=` swap; the `base_url` does not change. DeepSeek’s own guidance: keep base_url, just update model to deepseek-v4-pro or deepseek-v4-flash.

How to compare benchmarks honestly

Benchmark tables are where comparison tools mislead readers. Versions drift, runs differ, and a single number rarely captures real-world behaviour. Use this rule: cite the benchmark, name the version of every competitor, and prefer the model’s own report cross-checked against an independent source.

Coding

This is where V4-Pro looks strongest right now. On LiveCodeBench it scores 93.5, ahead of Gemini 3.1 Pro (91.7) and Claude Opus 4.6 (88.8). Its Codeforces rating of 3206 also tops GPT-5.4 (3168). On SWE-Bench Verified, third-party reporting puts DeepSeek V4 Pro at Terminal-Bench 67.9% vs Claude 65.4%, LiveCodeBench 93.5% vs 88.8%, and SWE-bench 80.6%. Cross-check the exact split (Verified / Full / Lite) in DeepSeek’s V4 technical report before quoting a single SWE-Bench number in production decisions.

Reasoning, math and knowledge

On math benchmarks, V4-Pro is strong. IMOAnswerBench (89.8) is its best result relative to peers — well ahead of Claude (75.3) and Gemini (81.0), though GPT-5.4 edges ahead at 91.4. HMMT 2026 is the one benchmark where Claude (96.2) and GPT-5.4 (97.7) pull decisively ahead of V4-Pro (95.2). On general knowledge, DeepSeek acknowledges directly that V4-Pro leads all open models but trails Gemini-3.1-Pro on rich world knowledge.

Independent intelligence indices

Artificial Analysis, an independent benchmark aggregator, scores V4-Pro in the upper tier of open-weight models. DeepSeek V4 Pro (Reasoning, Max Effort) scores 52 on the Artificial Analysis Intelligence Index, placing it well above average among other open weight models of similar size (median: 28). Throughput is unremarkable — it generates output at 33.5 tokens per second, at the lower end compared to other open weight models of similar size (median: 55.1 t/s) — so latency-sensitive products should test before committing.

Thinking modes: a parameter, not a model

One of the biggest changes from V3.x is that thinking mode in V4 is no longer a separate model ID. Both V4 tiers expose three reasoning settings via a single request parameter:

• Non-thinking (default) — fastest, cheapest. Supports FIM completion (Beta).
• Thinking (high) — set reasoning_effort="high" with extra_body={"thinking": {"type": "enabled"}}. The response returns reasoning_content alongside the final content.
• Thinking (max) — reasoning_effort="max". Requires enough output budget; set max_tokens high to avoid truncation.

Chat requests hit POST /chat/completions, the OpenAI-compatible endpoint. Here is a minimal Python example using the OpenAI SDK against V4-Pro in thinking mode:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.deepseek.com",
    api_key="YOUR_KEY",
)

resp = client.chat.completions.create(
    model="deepseek-v4-pro",
    messages=[{"role": "user", "content": "Plan the migration."}],
    reasoning_effort="high",
    extra_body={"thinking": {"type": "enabled"}},
    max_tokens=8000,
)

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

The API is stateless. You must resend the conversation history with every request — unlike the web chat, which maintains your session. DeepSeek also exposes an Anthropic-compatible surface against the same base URL, so the Anthropic SDK works by swapping base_url and api_key.

Worked cost comparison: same job, three models

Comparison tools fail when they quote a per-token rate without showing the math. Here is the same workload — 1,000,000 calls with a 2,000-token cached system prompt, a 200-token user message and a 300-token response — costed against both V4 tiers.

deepseek-v4-flash

• Cached input: 2,000 × 1,000,000 × $0.028/M = $56.00
• Uncached input: 200 × 1,000,000 × $0.14/M = $28.00
• Output: 300 × 1,000,000 × $0.28/M = $84.00
• Total: $168.00

deepseek-v4-pro

• Cached input: 2,000,000,000 × $0.145/M = $290.00
• Uncached input: 200,000,000 × $1.74/M = $348.00
• Output: 300,000,000 × $3.48/M = $1,044.00
• Total: $1,682.00

Pro is roughly 10× the spend of Flash on this shape of workload. For competitive context, OpenAI’s GPT-5.4 costs $2.50 per 1M input tokens and $15.00 per 1M output tokens, while Claude Opus 4.6 costs $5 per 1M input tokens and $25 per 1M output tokens. DeepSeek delivers similar performance to these models at a 50-80% cost reduction. Verify both providers’ pricing pages before committing — rates change.

Decision framework: which model when

Use this checklist. It is the framework I run on every new DeepSeek deployment.

1. Default to V4-Flash. For chat, summarisation, extraction, classification and most coding assistance, Flash hits the price-quality knee. Move up only when you have measured a quality gap on your own evaluations.
2. Choose V4-Pro when your workload is agentic coding, multi-step automation across long contexts, or knowledge-intensive analysis where benchmark deltas (LiveCodeBench, Terminal-Bench, IMOAnswerBench) translate to fewer wrong outputs. Expect ~7–10× the per-call spend.
3. Enable thinking mode when answers must be correct on the first attempt — math, planning, multi-file refactors. Skip it for streaming chat and short user replies.
4. Migrate off legacy IDs before 2026-07-24 15:59 UTC. They route to V4-Flash today, but requests will fail after the cutoff.
5. Compare against GPT-5 and Claude 4 only with current pricing pages open. The competitor numbers above are accurate as of April 25, 2026 but move month to month.

Comparison-tool gotchas that quietly break your math

• Mixing tiers in one cost calc. Pick V4-Flash or V4-Pro — never blend rates.
• Forgetting the uncached input bucket. A cached system prompt does not cover the new user message on each call. Always include three buckets in any estimate.
• Quoting V3.2 prices as current. V4 superseded V3.2 on April 24, 2026 — the V3.2 rates ($0.28 miss / $0.42 output) are historical now.
• Believing “no daily message cap” on the web chat. DeepSeek does not publicly document a cap, but absence of documentation is not the same as a guarantee.
• Treating off-peak discounts as active. They ended on September 5, 2025 and have not returned with V4.

For follow-up reading, the DeepSeek pricing calculator turns the math above into per-day and per-month estimates, and the DeepSeek cost estimator compares scenarios across both tiers. If you want benchmark depth rather than pricing, the DeepSeek benchmarks 2026 page tracks third-party verification as new runs land.

Last verified: 2026-04-25. 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 V4 technical report before committing to a production decision.