Kimi vs DeepSeek

Kimi vs DeepSeek comes down to which mistakes your app can afford: start with Kimi K3.1 Pro from Moonshot AI for difficult, long agent runs, and DeepSeek V4.1 Flash for cheap routing and bounded tasks. Both families offer roughly 1 million tokens of context, so capacity alone does not decide the choice. For self-hosting, DeepSeek's MIT license is the simpler starting point; Kimi's downloadable weights carry commercial conditions.

Kimi vs DeepSeek: Which Should You Pick?

Choose Kimi K3.1 Pro when finishing a complicated task matters more than minimizing each call. A founder building an agent that reads a specification, changes a codebase, checks the result and repairs mistakes should put it first on the evaluation list. That recommendation rests on Moonshot's published long-task evidence, not a production test performed for this comparison.

Choose DeepSeek V4.1 Flash for the routine workload. A support app classifying requests, selecting a tool or extracting fields has a different buying criterion: pass a clear acceptance test at low cost. Flash also offers a non-thinking mode; Kimi K3.1 Pro always reasons before answering.

Choose DeepSeek first when distributing or self-hosting weights is the deciding requirement. Keep DeepSeek V4 Pro when your own workload demonstrates a reason to pay more than Flash. Keep Kimi K3 when an existing evaluation or deployment depends on that checkpoint; K3.1 Pro is the stronger default candidate for a new Kimi integration.

Verified against both makers' live documentation and model repositories on 5 November 2026. The criteria are task completion evidence, model and tool billing, integration requirements, and weight-license conditions. No hands-on model benchmark was performed for this page.

All prices below are USD per million tokens, ordered as uncached input / cached input / output. They come from Moonshot's model inference pricing and DeepSeek's current models and pricing. Kimi cache-write charges are additional billing categories described below.

The important naming correction: deepseek-flash serves DeepSeek-V4.1-Flash-1017. The old deepseek-v4-flash identifier is still accepted but routes to that newer model. deepseek-v4-pro remains DeepSeek-V4-Pro-0813. A familiar API string does not guarantee a frozen checkpoint.

Long Agent Runs: Kimi K3.1 Pro Gets the First Trial

Kimi K3.1 Pro is Moonshot's current model for demanding agent work, where software repeatedly asks the model what to do, executes a tool, and returns the result. Winner for the first long-run evaluation: Kimi K3.1 Pro. The key distinction is sustained problem-solving, not having a bigger context window than DeepSeek.

Moonshot reports 65.7% on Terminal-Bench 4.0, a terminal-task evaluation, and 61.2% versus K3's 55.95% on its internal Kimi Code Bench 3.0 Long-horizon test. The internal result measures Moonshot's own task set, so it is evidence of improvement within that family rather than a universal completion rate. See the K3.1 Pro launch evidence.

For a mid-market CTO, the useful evaluation is an unfinished business process: reconcile conflicting documents, produce a report, then correct it after a reviewer changes an assumption. Check whether the agent preserves requirements after tool failures and whether the final artifact passes review. A convincing first answer is only one part of that job.

DeepSeek remains a serious coding candidate. Its October Flash model card reports 42.3 on Terminal-Bench 4.0 and 77.0 on DeepSWE v1.1. Moonshot's K3.1 Pro model card reports 75.5 on the latter, but the makers use different agent harnesses there: the surrounding software that supplies tools and manages the loop. That is a reason to compare your actual harness, not declare one family the winner of every coding task.

A million-token context is the amount a model can hold in a request, not a promise that every buried instruction will survive a long workflow. Preserve a compact task state, explicit acceptance criteria and recoverable checkpoints even when the full history fits. If Flash passes your complete-task test, use it; promote difficult cases to Kimi only when the extra capability changes the result.

Tool Use: DeepSeek for Built-In Web Access, Kimi for a Custom Loop

DeepSeek's current Flash model combines vision, tool calling and a choice of thinking mode at a low API rate. Winner for a new web-connected agent using the maker's search services: DeepSeek. Its Search and Fetch services are documented, separately metered tools; your agent still needs to decide when to call them and what to do with the returned material.

The important Kimi limitation is easy to miss: its current model guide says web search is being updated and is not recommended for production workflows in the near term. That does not mean Kimi cannot call your own search function. It means a production design should separate the model's tool-calling ability from the readiness of Moonshot's supplied search service.

The K3.1 Pro integration guide also requires the complete assistant message to be returned on subsequent turns, including reasoning_content and tool_calls. Vision input accepts base64 or Moonshot file references, not public image URLs. These are concrete migration requirements for a wrapper that previously kept only the final text.

Kimi K3 already supports custom tools, vision and strict structured output, as documented in its K3 API guide. K3.1 Pro's upgrade is not the invention of tool calling. The reason to upgrade is the quality of the decisions made across the loop.

For a senior builder connecting an internal ticket system, both families can request a function call. Your application owns execution, authentication, retries and approval of consequential actions. Moonshot's Formula tooling guide describes a separate execution step for its official tools; DeepSeek's function-calling guide likewise shows the developer returning results to the model. Downloading weights does not download either vendor's hosted search engine.

API Billing: DeepSeek Wins the Routine Workload

Winner on the published token rates: DeepSeek Flash. At the listed rates, both DeepSeek models remain cheaper than K3.1 Pro for each equivalent token category, even during peak hours. Kimi earns its premium only if it completes work that the cheaper option fails, or reduces costly retries and human repair.

Kimi AI Pricing

K3.1 Pro's standard input and output rates are one-third below K3's. For a new integration, paying K3's higher rates needs a specific regression or deployment reason. The broader product and subscription distinctions live in our Kimi pricing guide.

Cache billing deserves a separate check. A cache stores a repeated prompt prefix so it can be reused. K3.1 Pro lists cache writes at $2 per million tokens for five minutes or $4 for one hour; K3 lists $3 and $6 respectively. Hits use the cached-input rates in the table.

Do not count the same input token twice. Moonshot's cache accounting documentation divides total input into uncached, cached and cache-write tokens. The write is its own category, not another ordinary input charge on the same token. The default cache lifetime is five minutes, and a hit renews it without another write fee. A human review pause can change the useful cache lifetime even when the prompt remains identical.

DeepSeek AI Pricing

DeepSeek's peak windows are 01:00-04:00 and 06:00-10:00 UTC, Monday through Friday, excluding Chinese public holidays. Other times use off-peak pricing. Schedule flexible background work accordingly; a customer-facing request cannot always wait for a cheaper period. Our DeepSeek pricing guide carries the detailed workload calculations.

For a solo builder, Flash is the sensible base layer for classification, extraction and tool selection once it passes the app's acceptance tests. A larger company should also measure accepted outcomes, response time and repair effort. A lower token invoice is not a saving if operators spend their day correcting its output.

Search and Fetch Billing Since November 1

Budget Search and Fetch separately from model tokens. Each costs $0.75 per 1,000 successful requests. DeepSeek's Search and Fetch billing rules charge only an HTTP 200 response that produces non-empty search results or fetched page content. Empty results, failures and timeouts are not billed; requesting more search results does not change that request's price.

The October release notice ended the free period for server-side Search and Fetch calls through DeepSeek Harness, Claude Code and Codex at 16:00 UTC on October 31, 2026, equivalent to 00:00 Beijing time on November 1. Those integrations now incur the per-request fee too. The dated transition in that notice specifically concerns the server-side integrations; it should not be read as a promise that every standalone API call was previously free.

For an illustrative research workload, 1,000 successful searches plus 5,000 successful page fetches cost $4.50 in tool fees: 6,000 multiplied by $0.75 per thousand. Model-token processing is a separate bill. A search that finds several useful pages is one search request; fetching those pages individually creates several fetch requests.

Open Weights and Licensing: DeepSeek Is Simpler

Winner for straightforward weight redistribution and commercial self-hosting: DeepSeek. Open weights means you can obtain the model's learned parameters. The accompanying license determines what you may do with them, and a download does not make a model economical to operate.

Moonshot's launch post promised K3.1 Pro's full weights by November 4. At verification on November 5, its official K3.1 Pro file repository was public and ungated, and the repository metadata listed all 96 safetensors weight shards. That establishes published weights now. A repository's creation timestamp is not its public-release timestamp.

Both the Kimi K3 license and Kimi K3.1 Pro license impose conditions beyond a standard MIT grant:

• A licensee or affiliate operating a model-as-a-service business needs a separate agreement before commercial use if aggregate revenue exceeds $20 million over any consecutive 12 months. The definition excludes model features embedded in end-user products and mere relaying of requests.
• A commercial product or service above 100 million monthly active users or $20 million in monthly revenue must prominently display the relevant Kimi model name.
• Those two provisions have exemptions for defined internal use and access through Moonshot's official products or certified inference partners. Internal use excludes making the model, its outputs or capabilities available to third parties.

An app with a bounded document-analysis feature and an inference platform selling configurable model access are different cases under that wording. Do not turn the annual revenue condition into a blanket claim that every company above that size needs a separate license.

DeepSeek's V4 Pro repository, original V4 Flash repository and current Flash card identify the code and weights as MIT-licensed. That removes Kimi's particular commercial thresholds from the weight-license decision; it does not remove the operating cost or your application's obligations.

Hosting: Choose the Checkpoint as Carefully as the Provider

Use the official API first unless control of the runtime is itself a requirement. For most app builders, that is the quickest way to establish whether the model can do the job before taking on hardware and serving work.

There are three practical routes:

• Maker-hosted API: use Moonshot or DeepSeek directly, with the rates and model mappings above. Record the model version behind any moving alias in your evaluation notes.
• Third-party managed hosting: ask for the exact checkpoint, precision, supported tools, context limit, data location and billing terms. An OpenAI-compatible endpoint describes a request format; it does not guarantee feature or model parity.
• Your own or rented infrastructure: choose released weights, an inference engine and a supported serving recipe. You own capacity, updates, observability and the tool execution layer.

Moonshot's K3.1 Pro model card also lists 2.8 trillion total parameters, 104 billion activated per token, MXFP4 weights and MXFP8 activations, and recommends vLLM, SGLang or TokenSpeed. Activated parameters describe computation per token, not the complete model you need to store. Treat this as infrastructure planning, not a casual laptop installation.

DeepSeek's V4.1 Flash architecture card describes 552 billion backbone parameters plus 196 billion conditional-memory parameters. Cheap hosted inference does not mean a tiny self-hosted model. Its current release provides prompt-encoding and inference instructions; preserving those formats matters for tool calls and thinking mode.

For self-hosting, a version mismatch also matters: DeepSeek's original V4 Pro weight card describes a preview release, while the current API lists the later 0813 revision. A matching family name is not proof that downloaded weights reproduce the current hosted model. Establish checkpoint parity before treating a local result as an API benchmark.

Switching from Kimi to DeepSeek, or Back

Move the application contract before moving all traffic. A switch changes more than the base URL: tool schemas, message history, image encoding, reasoning settings, cached prefixes and the endpoint's current checkpoint all affect behavior.

1. 1

   Choose complete tasks with pass conditions

   Use representative work from your app: a ticket correctly routed, a code change that passes checks, or a document extraction accepted by the downstream system. Include a tool error and a resumed long task. Decide what success means before comparing outputs.

2. 2

   Port the message and tool loop

   Preserve full assistant messages where required, match every tool result to its call, and adapt image inputs. Keep vendor-specific options in an adapter so they do not spread through business logic.

3. 3

   Compare outcomes and total expense

   Record completed work, retries, human repairs, model tokens and successful Search/Fetch requests. Reusing a prompt does not transfer a provider's cache. Keep the old endpoint available until the new route passes the same acceptance conditions.

Do not switch a reliable long-running agent solely because a price card looks better. First try Flash on a bounded subtask. Conversely, do not move every classification request to Kimi because it leads a demanding benchmark. Kimi is an alternative to DeepSeek when its stronger result earns the premium; DeepSeek is an alternative to Kimi when the task's quality floor is already met.

Kimi K2 vs DeepSeek

Older comparisons are a poor deployment plan for a new account. Moonshot says K2-series access closed to new users on October 31, with retirement planned by the end of December 2026. Existing K2 users should evaluate the current K3 family and current Flash model, rather than assume their old endpoint will remain a long-term option.

Kimi vs DeepSeek vs Qwen

If neither finalist fits your deployment requirements, broaden the shortlist through our Chinese AI models guide. Keep the same workload and licensing criteria when adding another family; a larger shortlist is useful only if it resolves a concrete requirement.

DeepSeek vs Kimi: Frequently Asked Questions

Kimi AI Free Access and Other Buying Questions

Is Kimi AI better than ChatGPT?

This comparison does not establish a general ChatGPT winner. For the builder choosing between the two open-weight families here, K3.1 Pro earns a long-task trial; the final choice should follow complete-task acceptance tests.

Is there any AI better than DeepSeek?

For a demanding long agent workflow, Kimi K3.1 Pro is a credible upgrade candidate based on Moonshot's published results. For a bounded task that Flash already completes reliably, the more expensive model may provide no useful improvement.

Is Kimi better than Codex?

The model and the software around it are different choices. A benchmark run using a particular agent harness does not establish that an entire product is better. Compare the complete workflow you intend to use, including its tools and checks.

Is Kimi actually good?

Moonshot publishes evidence for difficult coding and multi-step work, including long-horizon improvements over K3. Treat those results as a reason to evaluate K3.1 Pro, not as a guarantee for your app's tasks.

Is Kimi the best Chinese AI?

There is no single winner across these jobs. K3.1 Pro gets the first trial for demanding long runs, DeepSeek Flash wins the low-cost routing role, and DeepSeek is simpler for weight licensing.

Is Kimi actually free?

Do not budget a production app on consumer free access. The developer API is billed by usage, separately from Kimi membership and Kimi Code, as explained in Moonshot's product and billing comparison.

How much does Kimi AI cost?

For the API models in this comparison, use the per-million-token table above and include the relevant cache-write category. Consumer memberships and coding subscriptions are separate purchases; they are not interchangeable with this API bill.

Is the Kimi app safe?

A model-quality result does not answer a data-handling question. For company information, choose the deployment route first and review its data terms, access controls and retention settings. An open-weight license does not describe the consumer app's data handling.

How much can I use Kimi for free?

This API comparison does not promise a fixed consumer free quota. Check the current entitlement shown in the product. For an app or agent you operate, plan around metered API usage or the infrastructure cost of self-hosting.

Your Monday move: put Flash on one clearly bounded route, evaluate K3.1 Pro on the difficult end-to-end task, and record accepted results plus repair effort. If self-hosting is mandatory, resolve the license and exact downloadable checkpoint before ordering capacity.

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