Pricing and free-tier disclosure. Numbers in this article are taken from Mistral's La Plateforme (free experimentation tier and paid tier), the DeepSeek API pricing page, and the Ollama model library as of June 2026. APIs change pricing and quotas frequently — always check the official pages for current numbers before committing to a model. OpenClaw Easy makes both Mistral and DeepSeek usable as drop-in providers, so this comparison is provider-neutral; we are not paid by either lab.

Mistral (French lab, founded 2023) and DeepSeek (Chinese lab, behind the V3 and R1 models) are the two most-discussed open-weight model families of 2026. Both ship strong general-purpose models. Both publish open weights you can run locally. Both have an API with generous pricing relative to OpenAI and Anthropic. And both are realistic free-or-near-free options for a chatbot project.

This guide compares them head-to-head for the most common use case people ask us about: powering an AI chatbot on WhatsApp, Telegram, Slack or Discord, on a budget. We focus on what matters for a chatbot — reasoning quality, response speed, multilingual support, the practical free path, and what your laptop can actually run locally.

The 30-second answer

  • Pick DeepSeek R1 if your chatbot needs real reasoning — math, code debugging, multi-step logic, careful planning. R1's chain-of-thought style gives noticeably better answers on hard questions. It is also the better pick for fully-local privacy: pull the distilled 7B variant via Ollama and you have a private reasoning model on your laptop for free.
  • Pick Mistral if your chatbot mostly does general conversation, FAQs, summaries, or European-language support, and you want fast replies with a generous free hosted tier. Mistral Small is fast enough to feel snappy on WhatsApp, La Plateforme has a free experimentation tier, and the open-weight Mistral 7B runs well on a 16 GB laptop.

If you cannot decide, OpenClaw Easy lets you wire up both and pick a different model per channel — there is no need to commit. Skip to the setup section below.

Mistral vs DeepSeek side-by-side

The two families ship different sets of models and different routes to "free". Here is the comparison.

Mistral DeepSeek
Current versions Mistral Small, Mistral Medium, Mistral Large 2 (hosted); Mistral 7B and Mixtral 8x7B (open-weight) DeepSeek V3 (general chat), DeepSeek R1 (reasoning); R1 distilled in 1.5B, 7B, 8B, 14B, 32B, 70B sizes (open-weight)
Where to run La Plateforme API (free dev tier + paid) and Ollama locally for the open-weight variants DeepSeek API (paid, very cheap) and Ollama locally for the distilled R1 variants
Free hosted tier Yes — La Plateforme experimentation tier with rate limits, free for low-volume bots No free API credits; pay-as-you-go from the first token
Hosted cost per 1M input tokens Mistral Small ~$0.20, Mistral Medium ~$2.70, Mistral Large 2 ~$2.00 (cache miss) DeepSeek V3 ~$0.27 (cache miss), DeepSeek R1 ~$0.55 — among the cheapest reasoning models on the market
Local-runnable size Mistral 7B (~4 GB on disk, fits 16 GB RAM); Mixtral 8x7B needs 48+ GB DeepSeek R1 distilled 7B (~4.7 GB, 16 GB RAM); 14B and 32B for stronger machines
Local-runnable full size? Mistral Small / Medium / Large not open-weight Full DeepSeek V3 / R1 (671B MoE) are too big for consumer hardware — local means distilled
Reasoning depth Solid for one-shot answers; no visible chain-of-thought R1 produces explicit chain-of-thought reasoning, strong on math and code
Speed (hosted) Mistral Small is one of the faster models in this class — sub-second first-token latency V3 is slower per-token than Mistral Small; R1 spends extra tokens "thinking" before answering
Multilingual quality Strong on European languages (French, German, Spanish, Italian), trained heavily on EU data Strong on Chinese and English; decent on most other major languages
Code generation Mistral Medium and Large are competent; Codestral exists as a separate code-focused model DeepSeek V3 and R1 are excellent at code; widely used for AI coding assistants
License (open-weight variants) Mistral 7B / Mixtral are Apache 2.0; Small/Medium/Large are proprietary DeepSeek V3 and R1 weights are released under permissive licenses allowing commercial use
Best for a chatbot Fast general-purpose AI on WhatsApp / Telegram; European-language support; quick FAQs Reasoning-heavy assistants; coding helpers; fully-local privacy with R1 distilled

Free options each offers

The phrase "free AI chatbot" actually means two different things, and Mistral and DeepSeek each solve a different one.

Mistral's free path is hosted. Sign up for La Plateforme at console.mistral.ai, verify a phone number, and you get an experimentation tier with rate-limited free access to Mistral Small, Medium, and several open-weight models. For a low-volume personal chatbot or a small-team Telegram assistant — say a few hundred messages per day — the free tier is enough. You do not need a credit card. You also get the option to download Mistral 7B and run it locally if you blow past the quota.

DeepSeek's free path is local. The DeepSeek API has no free credit tier — it is pay-as-you-go from the first token. But the per-token prices are exceptionally low (DeepSeek V3 input is roughly one-tenth the price of GPT-4o), so a hobby-scale chatbot costs cents per month. More importantly, the distilled R1 variants (1.5B through 70B) are open-weight and run locally via Ollama. The 7B variant fits on a 16 GB laptop and gives you a free reasoning model that never sends a message off your machine. For privacy-sensitive use cases, that is the more interesting "free".

Practical take. If "free" means "no credit card and a hosted API key", Mistral is the simpler start. If "free" means "no cost ever, no data leaves my machine", DeepSeek R1 7B on Ollama is the better pick. See our guide to free model options inside OpenClaw Easy for the full menu.

Reasoning — DeepSeek R1's chain-of-thought edge

For a chatbot that answers "what is the weather like" or "summarize this article", reasoning depth is irrelevant. Both models are fine. The gap opens when users ask harder questions.

DeepSeek R1 was trained specifically as a reasoning model. When you ask it a hard question — multi-step math, code debugging, planning a flow with branching conditions — it produces a visible chain-of-thought before the final answer. It will literally write out "let me think about this step by step", work through cases, and only then commit. On publicly reported benchmarks for math (AIME, MATH) and competitive coding (Codeforces), R1 is competitive with reasoning models that cost 10x as much per token.

Mistral's hosted models do not produce visible reasoning chains in the same way. They are tuned for fast, one-shot answers. For a customer support FAQ bot or a quick-reply assistant, that is a feature — users do not want to read three paragraphs of reasoning. For a Telegram bot that helps users solve calculus problems or debug Python, R1 is the more reliable pick.

A useful rule of thumb: if your chatbot's users frequently send questions that start with "how do I", "why does this", or "solve this", R1's reasoning is worth the slower response. If they mostly send "what is" or "tell me about", Mistral Small's speed wins.

Speed — Mistral Small fast, DeepSeek V3 slow but smart

Latency matters for a chatbot in a way it does not matter for a code assistant. Users on WhatsApp expect a reply within a few seconds. Anything beyond ten seconds feels broken.

Mistral Small is among the faster hosted models. First-token latency is typically under a second, and total response time for a short reply is two to three seconds end-to-end. That is roughly comparable to GPT-4o-mini and Gemini Flash. For a high-throughput chatbot or one that runs in a noisy group chat where you need replies to feel real-time, Mistral Small is the right shape.

DeepSeek V3 is slower per token. It is a Mixture-of-Experts model with 671B parameters total but only ~37B active per token, and the routing adds overhead. Expect three to six seconds for a short reply on the hosted API. DeepSeek R1 is even slower because it spends extra tokens on reasoning before producing the answer — a non-trivial response can take 10-20 seconds. R1 is not the right pick for an Instagram-comment-style auto-reply bot.

For locally-run distilled R1, throughput depends on your hardware. On an M2 MacBook Air, R1 7B generates roughly 25 tokens per second, which is fast enough for a personal chatbot but noticeably slower than cloud APIs.

Local sizes — what your laptop runs

Both families publish weights that fit on a regular laptop, but the trade-offs are different.

Mistral 7B is the headline open-weight model. About 4 GB on disk in default Q4 quantization, runs on 16 GB of RAM (or 8 GB with quantization tricks), Apache 2.0 license. It is a balanced general-purpose model — strong at chat, decent at code, no special reasoning capability. Pull it with ollama pull mistral.

Mixtral 8x7B is the bigger open-weight Mistral — a Mixture-of-Experts model with 46B total parameters. It needs roughly 48 GB of RAM and is overkill for a chatbot on a laptop. It is also no longer the frontier of the Mistral family; the more capable Mistral Medium and Large are hosted-only.

DeepSeek R1 distilled 7B takes about 4.7 GB on disk and runs on 16 GB of RAM. Pull it with ollama pull deepseek-r1:7b. The distilled variants are smaller models (based on Qwen or Llama) that have been fine-tuned on R1's reasoning traces — so you get most of R1's reasoning behavior at a fraction of the size. There are 1.5B, 8B, 14B, 32B, and 70B variants for different hardware budgets.

Full DeepSeek V3 and R1 are 671B parameters. On consumer hardware they are simply not runnable — you need a server with 800+ GB of RAM or a multi-GPU rig. If you want the full V3/R1, you use the hosted API; if you want local DeepSeek, you use a distilled variant.

Multilingual

Mistral was trained heavily on European languages and shows it. French, German, Spanish, and Italian responses from Mistral Medium and Large feel native — vocabulary choices, idioms, and register are clearly handled by a model that read a lot of EU text. If your chatbot serves users in any of those languages, Mistral has a real quality edge.

DeepSeek is strongest on English and Chinese — unsurprising given the lab's location and training data. Chinese-language responses from V3 are excellent, comparable to leading proprietary models. Major-language coverage (Spanish, Portuguese, Japanese, Korean) is solid but generally a step behind Mistral on European languages. If your chatbot is bilingual English/Chinese, DeepSeek is the natural pick.

For a chatbot serving global users on Telegram, you can mix: route messages by language detection to the better model. OpenClaw Easy supports configuring different agents per channel, so a French-language WhatsApp number can use Mistral while an English Telegram group uses DeepSeek.

Setup with OpenClaw Easy

OpenClaw Easy is a free desktop app that connects an AI provider to messaging channels (WhatsApp, Telegram, Slack, Discord, Feishu, Line). It is provider-agnostic — Mistral and DeepSeek are both first-class options, and you can configure them side-by-side.

Mistral (hosted, free tier):

  1. Sign up at console.mistral.ai and create an API key.
  2. In OpenClaw Easy, open the AI Provider settings and paste the key under Mistral.
  3. Pick a model in Agent Config — start with mistral-small-latest for fast chatbot replies.

DeepSeek R1 (local, free, fully private):

  1. Install Ollama.
  2. Pull the distilled R1 model: ollama pull deepseek-r1:7b.
  3. In OpenClaw Easy, point the AI Provider at the local Ollama endpoint (http://localhost:11434 by default).
  4. In Agent Config, pick deepseek-r1:7b from the auto-discovered model list.

OpenClaw Easy supports multiple agents and per-channel model assignment. A common setup: Mistral Small for the WhatsApp QR-paired channel (fast replies), and DeepSeek R1 on Ollama for a Telegram bot that handles math and code questions. The full local LLM workflow is covered in our Ollama on WhatsApp guide.

# Quick start: pull both ollama pull mistral ollama pull deepseek-r1:7b # Verify they're loaded ollama list

When DeepSeek is the better choice

  • Your chatbot needs to solve hard problems — math, logic puzzles, multi-step planning, debugging.
  • You want a local, private reasoning model on a laptop with 16 GB of RAM — R1 distilled 7B is the best free option in that slot.
  • Your use case is code generation or a coding assistant — DeepSeek V3/R1 are strong at code.
  • Your audience is Chinese-speaking or bilingual English/Chinese.
  • You want the cheapest possible hosted API for high-volume use — DeepSeek V3 input is around $0.27 per 1M tokens.

When Mistral is the better choice

  • You need a generous free hosted tier with no credit card for low-volume bot traffic — La Plateforme's experimentation tier is the easiest path.
  • Your chatbot needs to feel fast and snappy — Mistral Small responds in under three seconds, comparable to Gemini Flash.
  • You serve users in French, German, Spanish, or Italian — Mistral's European-language quality is noticeably stronger.
  • Your use case is general conversation, FAQ answering, or summarization — not reasoning-heavy — and reasoning chains would slow you down.
  • You want a permissively licensed open-weight 7B for fully-local use with a clear commercial license (Apache 2.0).

Frequently asked questions

Is Mistral really free?

Mistral's La Plateforme has a free experimentation tier that gives you API access to Mistral Small, Medium, and several open-weight models with rate limits. It is free for development and low-volume chatbot use. You will need to switch to a paid tier once your traffic exceeds the free quota, or run an open-weight Mistral model locally via Ollama for unlimited free use. The free tier requires phone verification and an account on console.mistral.ai.

Can I run DeepSeek R1 for free?

Yes. The distilled DeepSeek R1 variants (1.5B, 7B, 8B, 14B, 32B, 70B) are open-weight and you can pull them with Ollama using a command like ollama pull deepseek-r1:7b. Once downloaded, inference runs entirely on your machine with no API key and no per-token cost. The full DeepSeek R1 (671B parameters) is too large to run locally on consumer hardware, but it is available through the DeepSeek API at a low per-token price.

Which has better reasoning — Mistral or DeepSeek?

DeepSeek R1 has the clear edge on reasoning. R1 uses chain-of-thought style inference and was specifically trained for math, logic, and step-by-step problem solving — its public benchmarks are competitive with GPT-4 class models on those tasks. Mistral models (Small, Medium, Large) are strong general-purpose models with fast responses, but they do not produce the same visible reasoning chains as R1. For a chatbot doing FAQs, summaries, or short replies, Mistral is fast and good enough. For a chatbot that needs to solve math, debug code step-by-step, or handle multi-step logic, DeepSeek R1 is the better pick.

Can I use both in OpenClaw Easy?

Yes. OpenClaw Easy is provider-agnostic — you can configure a Mistral API key and an Ollama endpoint at the same time, then pick a different model per agent or per channel. A common setup is Mistral Small for fast WhatsApp replies and DeepSeek R1 (via Ollama) for a Telegram bot that handles math and code questions. You can switch models from the Agent Config dropdown at any time without reconnecting any channel.

Try OpenClaw Easy free

OpenClaw Easy is the fastest way to put either model into a real chatbot on WhatsApp, Telegram, Slack, or Discord. Paste a Mistral API key, or run Ollama with DeepSeek R1 — either way, you have a working AI chatbot in under ten minutes, for free. Download the desktop app and pick the model that fits your use case.

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