Best Open-Weight and Chinese LLMs for Coding and Reasoning

Answer First

For coding and reasoning, start with DeepSeek V4.1 Flash for agentic software work, Kimi K3 for persistent repository agents, Qwen3.8-Max for engineering tasks that mix code with visual or document evidence, and GLM 5.3 for defensive security review—then select the winner with one fixed repository test rather than headline specifications.

Coding and Reasoning Model Shortlist

This shortlist deliberately excludes price, input format, and maximum context from the primary decision. Those are deployment constraints; the first filter is whether the model produces correct patches, traces the right control flow, uses tools reliably, and explains its reasoning under the same test harness.

Model Selection Logic for Coding and Reasoning

1. Choose by task evidence: use repository repair, code review, terminal-agent, or security-analysis tasks instead of a generic chat prompt.
2. Separate coding quality from reasoning quality: score executable correctness, root-cause analysis, tool use, and adherence to constraints.
3. Treat price, input format, and context length as deployment constraints only after a model passes the coding-and-reasoning test.

DeepSeek V4.1 Flash: Coding Performance

DeepSeek V4.1 Flash is the coding-first DeepSeek candidate in this comparison. In the official model card, the max-effort evaluation reports 90.6 on Terminal-Bench 2.1, 74.2 on DeepSWE v1.1, 65.4 on NL2Repo-Bench, and a Codeforces rating of 3471. Those results make repository repair, terminal interaction, and codebase generation the right workloads to test first. They do not prove that the model will pass your own CI, so score executable patches and human correction time—not code style alone.

DeepSeek V4.1 Flash: Reasoning Performance

For reasoning, the same official release reports 90.9 on GPQA Diamond and 65.6 on MathArena Apex with reasoning_effort=100. That supports multi-step debugging, hypothesis formation, and tool-based investigation, while also showing why the effort setting belongs in every comparison record. Run a second test at the lower effort level you expect to use in production; otherwise the benchmark result and your deployed latency or cost profile will describe different systems.

Kimi K3: Coding and Reasoning Performance

Kimi K3 is the strongest candidate here for coding agents that must keep a plan coherent across many repository operations. Its published signals include 88.3 on TerminalBench 2.1, 81.2 on FrontierSWE, and 77.8 on ProgramBench; the same model page reports 91.2 on BrowseComp and 95.0 on DeepSearchQA for search-oriented reasoning. Test it on a task that requires inspection, editing, execution, and recovery from a failed attempt. A high score is useful evidence, but only your own agent scaffold can show whether it preserves constraints over a long run.

Qwen3.8-Max: Coding and Reasoning Performance

Qwen3.8-Max is most relevant when coding depends on more than source text. Its reported 86.6 on Terminal-Bench 2.1 shows strong terminal execution, while 67.7 on SWE-bench Pro suggests a tougher ceiling on repository repair. PaperBench at 93.0 and IFBench at 82.8 strengthen the case for tasks that combine code with documents, screenshots, diagrams, or detailed instructions. Use it for evidence-rich debugging, but keep patch correctness and test results as separate acceptance checks.

GLM 5.3: Coding and Security Reasoning

GLM 5.3 is a specialized security-reasoning candidate, not a general coding winner. Its reported 84.5% on CyberGym versus 54.4% on ExploitBench points to a clear pattern: vulnerability discovery is stronger than reliable exploit completion. That makes defensive code review, attack-surface mapping, and data-flow tracing the right first tests. Avoid extrapolating these security results to ordinary feature development until comparable repository-coding evidence is available.

Published Coding and Reasoning Benchmarks

The numbers below answer narrow questions about coding, reasoning, or security. They do not form one universal leaderboard because vendors use different harnesses, prompts, tool scaffolds, and reasoning settings. Use each result to design a test case, then compare accepted patches and verified explanations in your own environment.

A Fair Coding-and-Reasoning Test

Run every model with the same system prompt, repository snapshot, tool schema, timeout, retry rule, and acceptance test. Keep model-specific reasoning controls at their documented defaults unless the test is explicitly about those controls.

1. Repository patch: “Fix the failing pagination test without changing the public API. Return a patch and explain the root cause.” Accept only if the full test suite passes without a human patch.
2. Cross-file reasoning: “Trace the authentication flow across these files and identify the condition that permits an expired token.” Accept only if the model cites the correct files and control-flow path.
3. Tool-using agent: “Inspect the repository, propose a plan, edit the minimum files, run tests, and stop after two failed attempts.” Record tool-call validity, retries, and whether the agent obeys the stop condition.
4. Cost-sensitive triage: “Classify these 100 issues, identify duplicates, and recommend the 10 highest-risk bugs.” Measure accepted classifications per dollar, not price per token alone.

For each task, capture pass/fail, human corrections, input tokens, output and reasoning tokens, latency, retries, and total cost. The model with the lowest token price can still be more expensive if it needs more retries or review.

LLM API Cost per Accepted Task

Pricing checked September 14, 2026. The rates below are current CometAPI prices per 1M tokens. The example uses 100K input tokens and 10K output tokens with no cache hits, retries, tool charges, taxes, or account-specific discounts. CometAPI lists a 20% discount against the displayed official price for these routes; DeepSeek V4.1 Flash can also receive a 2× request multiplier during 01:00–04:00 and 06:00–10:00 UTC on weekdays.

At the checked base rates, DeepSeek V4.1 Flash is the cheapest route in this comparison at $0.0168 for the example workload. A matched 2× weekday window would raise that example to $0.0336. The ranking is still secondary to acceptance rate: a cheaper request is not cheaper work if it creates more failed patches, retries, or review.

A useful production metric is:

Cost per accepted task = model tokens + tool calls + retries + fallback spend + human review cost.

Comparing Chinese LLMs Through One CometAPI Integration

CometAPI gives the four-model shortlist one API key, one OpenAI-compatible base URL—https://api.cometapi.com/v1—and one billing workflow. That makes it practical to run the same coding-and-reasoning harness against each route without maintaining four provider integrations.

1. Get one CometAPI API key.
2. Set the OpenAI-compatible base URL to https://api.cometapi.com/v1.
3. Keep the task, repository snapshot, acceptance tests, and request shape fixed while switching the model ID among deepseek-v4.1-flash, kimi-k3, qwen3.8-max, and glm-5.3. Record model-specific reasoning settings and tool behavior with each result.

Production Error Handling with CometAPI

• 401 Unauthorized: confirm that the request uses a CometAPI key and the Bearer header.
• 404 Not Found: include /v1 in the base URL and copy the exact current model ID from the catalog.
• 429 or capacity errors: use exponential backoff, cap retries, and route to another tested model only when that model has already passed the same coding-and-reasoning acceptance test.
• Unexpected cost: inspect usage fields, reasoning effort, retries, cache behavior, and the weekday time-based multiplier windows for DeepSeek V4.1 Flash.
• Invalid model parameters: do not assume every OpenAI-compatible model accepts the same reasoning or sampling settings. Kimi K3, for example, documents fixed sampling behavior and thinking-only operation.

Final Recommendation

For most developer teams, DeepSeek V4.1 Flash is the first general coding-and-reasoning test because its official release publishes strong terminal, repository, and reasoning results. Add Kimi K3 when long-running agent continuity is the main risk, Qwen3.8-Max when engineering evidence includes documents or visual inputs, and GLM 5.3 when the task is defensive security analysis.

If open weights are a procurement requirement, DeepSeek V4.1 Flash has a published checkpoint and MIT license. Treat Kimi K3, Qwen3.8-Max, and GLM 5.3 as hosted comparison routes unless the exact checkpoint and license you intend to deploy are independently verified on publication day.

FAQ

Which Chinese LLM is best for coding?

Start with DeepSeek V4.1 Flash for a broad coding-and-reasoning evaluation, Kimi K3 for long-running repository agents, Qwen3.8-Max for evidence-rich multimodal engineering, and GLM 5.3 for defensive security review. The best production route is the one that passes your fixed repository tests with the least correction.

What is the cheapest model in this shortlist?

As of September 14, 2026, DeepSeek V4.1 Flash has the lowest published CometAPI base rates in this comparison. Its weekday time-based multipliers can change the effective request cost, so check the live model page before deployment.

Is Qwen3.8-Max open weight?

Hosted API access is confirmed on CometAPI, but a downloadable checkpoint and license were not verified for this article on August 26, 2026. Do not label it self-hostable until those artifacts are published.

Is GLM 5.3 open weight?

An open-weight release has been announced, while the current CometAPI page still notes that the public artifact is planned. Treat it as API-accessible and keep self-hosting on the watchlist until the weights and license are verifiable.

Which model supports image or video input?

DeepSeek V4.1 Flash accepts text and images, while Qwen3.8-Max is listed for text, image, PDF, and video input. Use those capabilities only when the coding task genuinely depends on visual or document evidence; test the exact CometAPI route before documenting production support.

Can I switch models without changing my infrastructure?

Usually yes. Keep the CometAPI base URL and API key, then change the model value. Retest model-specific parameters, multimodal payloads, reasoning controls, and tool behavior before production.

What is the difference between open source and open weight?

Open weight means the trained parameters are downloadable under a stated license. Open source is a broader claim that can include training code, data information, and reproducibility. Verify the actual checkpoint and license instead of relying on marketing labels.

Sources Checked

• CometAPI Quick Start
• CometAPI public model catalog API
• CometAPI base URL guide
• DeepSeek V4.1 Flash model page
• DeepSeek V4.1 Flash official model card
• Kimi K3 model page
• Qwen3.8-Max model page
• GLM 5.3 model page
• CometAPI pricing guide