DeepSeek V4 Pro vs Kimi K3: What Changed After the 0813 Update?

DeepSeek V4 Pro 0813 vs Kimi K3 at a Glance

The benchmark, speed, and latency figures above come from the current Artificial Analysis head-to-head evaluation. Its Intelligence Index uses the same evaluation framework for both models, which makes it more useful than placing two vendors' launch charts side by side.

What Changed in DeepSeek V4 Pro 0813?

DeepSeek V4 Pro 0813 is the current production version of V4 Pro, not a separate API product. DeepSeek's documentation states that the deepseek-v4-pro alias now points to DeepSeek-V4-Pro-0813 and that the calling method remains unchanged. The same documentation lists a one-million-token context window, up to 384K output, thinking and non-thinking modes, JSON output, and tool calling.

That naming decision matters because dated model names often force developers to change configuration files, routing rules, and evaluation records. Here, the upgrade happens behind the stable alias. On GPT Proto, the same principle applies: keep deepseek-v4-pro in your request.

The current independent result is also different from the numbers found in many July comparison articles. Artificial Analysis now gives the 0813 model an Intelligence Index score of 53 at maximum reasoning effort. Kimi K3 scores 60 under the current evaluation.

I would not describe that as a clean nine-point improvement from the older score of 44. The model changed, but benchmark suites and evaluation versions can also change. The defensible conclusion is narrower: comparisons based on the preview no longer describe the model served today, and fresh independent testing places 0813 closer to Kimi K3 than the preview-era coverage suggested.

DeepSeek has not changed the model's basic product shape. It remains a 1.6-trillion-parameter Mixture-of-Experts model with 49 billion parameters active per token, text input, a large output allowance, and selectable reasoning effort. The 0813 release is therefore best understood as a production and post-training upgrade rather than a new multimodal branch.

Benchmarks: Kimi K3 Still Leads, but Not Everywhere

Kimi K3 leads the broad independent comparison, 60 to 53. That seven-point difference matters when a workflow combines reasoning, knowledge, coding, and long-horizon planning. It does not mean Kimi will produce a better result on every repository or every prompt.

The two models make different trade-offs. Moonshot describes Kimi K3 as a 2.8-trillion-parameter native multimodal agent model designed for long-horizon coding and knowledge work. Its official model card documents text, image, and video understanding in the upstream model, plus a one-million-token context window. GPT Proto's current Kimi K3 route lists text, image, and document inputs.

DeepSeek V4 Pro 0813 is text-only, but it produces tokens at 83.2 tokens per second in Artificial Analysis testing. Kimi K3 reaches 40.8 tokens per second. DeepSeek also begins responding sooner, with a 1.63-second time to first token versus 2.97 seconds for Kimi.

In plain terms: Kimi has the higher overall capability score. DeepSeek returns text roughly twice as fast and costs far less while doing so.

There is another benchmark trap here. Vendor coding scores often use different agent setups, reasoning settings, repository snapshots, or pass criteria. A Kimi score produced with Kimi Code cannot automatically be compared with a DeepSeek score produced with another evaluation setup. For model selection, use independent matched evaluations as the shared baseline, then test the task that affects your product.

This article does not claim a private same-prompt coding test. Without one, declaring either model the universal coding winner would be stronger than the evidence allows.

Which Is Better for Coding and Agent Workflows?

For high-volume text-based coding, I would start with DeepSeek V4 Pro 0813.

The reason is not that it beats Kimi on every measure. It does not. The reason is that production coding agents repeatedly consume context and generate reasoning, patches, test plans, and tool instructions. Output pricing compounds across every loop. DeepSeek's selectable reasoning modes also let developers reserve maximum effort for difficult tasks instead of paying for the same reasoning behavior on every request.

That makes DeepSeek a strong default for:

• Repository reading and codebase Q&A

• Pull-request review

• Bug localization

• Refactoring plans

• Test generation

• Text-based tool calling

• High-volume background agents

• Structured JSON responses

Kimi K3 becomes more attractive when the cost of a failed attempt is larger than the token bill. Its higher independent intelligence score makes it a reasonable escalation model for long, difficult tasks that DeepSeek fails to complete. It is also the clear option when the request contains visual evidence.

A practical production pattern is therefore not “pick one forever.” Route routine text tasks to DeepSeek, then retry selected failures or multimodal tasks with Kimi. Because both are available through one GPT Proto API key and shared balance, the change can be limited to the model field.

DeepSeek V4 Pro vs Kimi K3 for Frontend Coding

“Frontend coding” covers two different workloads, and they should not be collapsed into one verdict.

The first is text-to-code: generating React components, CSS, page structure, accessibility fixes, or TypeScript logic from a written specification. There is not yet enough matched public evidence to claim that Kimi K3 or DeepSeek V4 Pro 0813 universally wins this category. DeepSeek's lower cost and faster output make it the more economical first attempt.

The second is visual frontend iteration: showing the model a screenshot, asking it to identify spacing or layout problems, and revising the interface from visual feedback. Kimi K3 is better suited to that workflow because it accepts image input. DeepSeek V4 Pro is text-only, so a developer would need to translate the screenshot into text or use a separate vision model first.

Use DeepSeek for text-described UI implementation at scale. Use Kimi when the model must see the interface.

API Pricing: DeepSeek Wins by More Than the Headline Suggests

GPT Proto currently prices DeepSeek V4 Pro at $1.044 per million input tokens and $2.088 per million output tokens. Kimi K3 costs $2.70 for input and $13.50 for output.

DeepSeek is 61.3% cheaper on input and 84.5% cheaper on output. The output difference is the important one for reasoning-heavy agents because their internal work and final responses can become long.

Token prices are still abstract, so consider two hypothetical workloads.

For the repository-scale example, Kimi costs about 3.88 times as much. That does not make Kimi poor value by definition. If it completes a difficult task in one attempt while a cheaper model needs repeated retries and human repair, the expensive call may still cost less overall.

The right production metric is accepted result cost, not token cost alone. Record the model, tokens, attempts, latency, test result, and reviewer acceptance for each task category. A low per-token rate only becomes a real saving when the output passes.

Speed and Latency

Artificial Analysis currently measures DeepSeek V4 Pro 0813 at 83.2 output tokens per second and Kimi K3 at 40.8. Time to first token is 1.63 seconds for DeepSeek and 2.97 seconds for Kimi.

These figures favor DeepSeek for interactive coding assistants and parallel agent workers. A model that returns tokens twice as quickly can shorten the visible wait even when both models eventually reach an acceptable answer.

However, provider speed is not a permanent property of the weights. It also depends on server load, quantization, batching, prompt length, reasoning effort, and where the request is served. Treat the current measurements as a comparable snapshot, not a latency guarantee for every call.

Context, Reasoning, and Deployment Differences

Both models support roughly one million tokens of context, so context size alone does not decide this comparison. How they use that context matters more.

DeepSeek supports non-thinking and thinking modes, with reasoning effort controls for harder tasks. It also allows up to 384K output according to the current DeepSeek API specification. This flexibility suits routing systems that use fast responses for simple tasks and deeper reasoning only when needed.

Kimi K3 uses always-on reasoning with configurable effort. Its larger 2.8T architecture and multimodal design target long-horizon work involving documents, code, images, and tool use. The trade-off is higher output cost and slower measured generation.

Both models have downloadable weights, but “open weight” does not mean identical licensing. DeepSeek V4 Pro uses the MIT license. Kimi K3 uses its own Kimi K3 License. Teams planning commercial self-hosting should review the exact Kimi terms rather than assuming they match MIT.

The hardware requirement is also far beyond an ordinary local workstation. For most development teams, hosted API evaluation is the realistic starting point even when weights are available.

How to Access DeepSeek V4 Pro 0813 and Kimi K3 with One API Key

GPT Proto exposes both models through an OpenAI-compatible chat completions endpoint. Start with DeepSeek by setting MODEL_ID to deepseek-v4-pro:

export GPTPROTO_API_KEY="your_api_key"
export MODEL_ID="deepseek-v4-pro"

curl --request POST "https://gptproto.com/v1/chat/completions" \
  --header "Authorization: Bearer $GPTPROTO_API_KEY" \
  --header "Content-Type: application/json" \
  --data "{
    \"model\": \"$MODEL_ID\",
    \"messages\": [
      {
        \"role\": \"user\",
        \"content\": \"Review this function for correctness and return the answer as concise Markdown.\"
      }
    ]
  }"

To send the same text request to Kimi K3, change one line:

export MODEL_ID="kimi-k3"

Then run the same curl request again. You do not need a separate Moonshot account, a second balance, or a DeepSeek version suffix.

For a controlled comparison, keep the prompt, context, reasoning setting, and output limit the same. Log the returned usage fields along with latency, attempts, and whether the result passed your tests. Comparing one attractive answer from each model is not enough for a production routing decision.

Which Model Should You Choose?

Choose DeepSeek V4 Pro 0813 when:

• Your workload is primarily text and code

• Output volume makes token cost important

• Low latency improves the user experience

• You want non-thinking and deeper reasoning under one model ID

• You need an MIT-licensed self-hosting path

• You are selecting a default model for high-volume agents

Choose Kimi K3 when:

• The model must inspect screenshots, images, or documents

• The highest current independent intelligence score matters more than price

• A failed task costs more than a premium API call

• You are building long-horizon multimodal agents

• You want to escalate the hardest requests after a cheaper model fails

For most developers, the best routing policy is DeepSeek first and Kimi when needed. It captures most of DeepSeek's cost and speed advantage without giving up access to Kimi's multimodal and higher-capability ceiling.

Final Verdict

DeepSeek V4 Pro 0813 is the better default for most text-based coding and agent workloads. It trails Kimi K3 by seven points on the current Artificial Analysis Intelligence Index, but it produces output at roughly twice the speed and costs far less on GPT Proto—especially for output-heavy agent loops.

Kimi K3 is the better specialist. Choose it when the task includes visual input, when the hardest possible reasoning matters more than the bill, or when DeepSeek has already failed and another attempt is cheaper than manual recovery.

The 0813 update does not make Kimi obsolete. It makes the routing decision sharper: DeepSeek for volume, speed, and text; Kimi for multimodality and the higher measured capability ceiling.