Kimi K2.6: The Complete Developer Guide (2026)

Last updated: May 4, 2026

Moonshot AI shipped Kimi K2.6 on April 20, 2026, four months after K2.5 and two days after a quiet preview drop. On paper it is another open-weights MoE release. In practice it is the first open-source model that can sustain a 13-hour autonomous coding session, coordinate 300 sub-agents on a single task, and trade SWE-bench Pro punches with GPT-5.5 — at roughly 5x lower cost than Claude Opus 4.7. It also ships under a modified MIT license, with native INT4 weights, a 256K context, and a 1T-parameter / 32B-active MoE architecture that runs on roughly the same hardware as DeepSeek V3 (V4 switched to a different attention scheme four days after K2.6’s launch).

This guide is for engineering leaders, founders, and developers deciding where K2.6 fits in a 2026 stack alongside Claude Opus 4.7, GPT-5.5, DeepSeek V4 Pro, and Qwen 3.5. We focus on what changed from K2.5, what the model actually costs to run, where it beats and loses to its peers, and the agent-swarm story that is genuinely new and not just bigger numbers on the same chart.

TL;DR

• What it is: Moonshot AI's flagship open-weights reasoning and agentic-coding model, released April 20, 2026. 1T total / 32B active MoE with 384 experts, MLA attention, 256K context, native multimodal (text + image + video input).
• Why it matters: 58.6% on SWE-bench Pro — tied with GPT-5.5, ahead of Gemini 3.1 Pro (54.2%) and Claude Opus 4.6 (53.4%). Claude Opus 4.7 still leads at 64.3%, but K2.6 narrows the gap to single digits while costing roughly 5–6x less per token.
• Where it loses: Opus 4.7 still leads on SWE-bench Verified by ~7 points (87.6% vs 80.2%) and on Humanity’s Last Exam without tools. GPT-5.5 leads the Artificial Analysis Intelligence Index (60 vs 54). DeepSeek V4 Pro (released four days after K2.6, April 24, 2026) leads on LiveCodeBench (93.5 vs 89.6), is competitive on SWE-bench Verified (80.6 vs 80.2), and is dramatically cheaper during its 75%-off promo through May 31, 2026.
• What is new: Agent Swarms (up to 300 sub-agents, 4,000 coordinated steps), Claw Groups for heterogeneous agent coordination with persistent memory, 13-hour long-horizon coding runs (4,000+ tool calls), 96.6% tool-invocation success rate, and a coding-driven design mode that ships front-end animations end-to-end.
• What broke / regressed: Nothing in the K2 line broke, but the new chat template adds an explicit thinking field; old K2.5 client code that ignored it will silently miss the reasoning trace. Native INT4 inference also requires Transformers >=4.57.1 — older inference stacks will OOM or fall back to FP16.
• Bottom line: Default to K2.6 for cost-sensitive coding agents, internal tools, and any workload where you need open weights. Reach for Opus 4.7 when the task is hard enough that one Opus run beats three K2.6 retries. Use GPT-5.5 when terminal/agent breadth matters more than coding depth.

What changed from Kimi K2 and K2.5

Moonshot has shipped three K2-class models in nine months: the original K2 in August 2025, K2.5 (then called K2-Thinking) in November 2025, and K2.6 on April 20, 2026. The cadence is fast enough that “should I upgrade” is a real question, not a reflex.

The headline gains over K2.5:

• Coding accuracy: +12% on internal CodeBuddy evals; SWE-bench Verified climbs from 76.8% (K2.5) to 80.2% (K2.6); SWE-bench Multilingual from 73.0% to 76.7%; LiveCodeBench v6 from 85.0% to 89.6%; Terminal-Bench 2.0 from 50.8% to 66.7%.
• Long-context stability: +18% on multi-hour autonomous sessions. K2.6 holds coherence across 4,000+ tool calls in a single run, where K2.5 typically drifted past ~1,500.
• Tool-invocation success: 96.6%, up from ~91% on K2.5. The remaining failure modes are mostly malformed tool schemas in third-party MCP servers, not model errors.
• Agent Swarms: Up to 300 concurrent sub-agents per task and 4,000 coordinated steps, vs 100 / 1,500 on K2.5. Combined with Claw Groups for heterogeneous coordination, this is the headline capability of the release.
• Vision: The MoonViT encoder grew to 400M params and now matches Opus 4.7 on dense-document tasks (MMMU-Pro 79.4%, MathVision-with-python 93.2%, V*-with-python 96.9%).
• Reasoning: AIME 2026 96.4%, HMMT 2026 92.7%, GPQA-Diamond 90.5% — pushes K2.6 past every other open-weights model on competition math and graduate-level science.

And the things to know before migrating:

• New chat template fields. thinking: {type: "enabled" | "disabled", keep: "all"} is now first-class. K2.5 client code that did not parse response.choices[0].message.reasoning will silently miss the reasoning trace. The extra_body shape differs slightly between Moonshot’s official API and vLLM/SGLang.
• Transformers version pin. Native INT4 inference needs transformers>=4.57.1, <5.0.0. Older stacks will silently fall back to FP16 (and OOM on most single-node setups).
• Default temperature is now 1.0. K2.5 defaulted to 0.6. If you copy K2.5 prompts forward without re-tuning, expect more creative variance in thinking mode.
• Vocabulary expanded to 160K. A modest reduction in tokens-per-request for code and non-English text vs K2.5’s 152K vocab.

For a deeper side-by-side with the closed-weights frontier, see our Kimi K2.6 vs Claude Opus 4.7 comparison and Kimi K2.6 vs GPT-5.5 comparison.

Architecture: MoE, MLA attention, vision encoder, agent swarms

K2.6 is built around four primitives that, together, define what “open-weights agentic coding” looks like in 2026.

Mixture-of-Experts with MLA attention

K2.6 is a 1-trillion-parameter sparse MoE with 32B active params per token. The expert layout: 384 routed experts plus 1 shared expert, 8 experts selected per token, 61 transformer layers (one of which is dense), 64 attention heads, attention hidden dim 7168, MoE hidden dim per expert 2048. The activation function is SwiGLU.

The attention mechanism is Multi-head Latent Attention (MLA), the same low-rank KV-projection scheme DeepSeek popularised. MLA is the reason K2.6 can hold a 256K context on commodity inference hardware: KV-cache memory is 5–10x lower than vanilla multi-head attention at the same context length, which makes 8x H100 / 8x H200 deployments practical for the full 256K window.

MoonViT vision encoder

MoonViT is Moonshot’s in-house ViT, scaled to 400M params for K2.6. It accepts images and video frames as input, projects them into the language-model embedding space, and is jointly trained end-to-end with the LM. The practical upgrade over K2.5: dense-document and dense-UI reading is now competitive with Opus 4.7. K2.6 reads full IDE screenshots, design tools, and dense data tables without losing detail.

Agent Swarms and Claw Groups

This is the genuinely new capability of the release. K2.6 ships with native orchestration primitives for spawning, coordinating, and collecting from up to 300 sub-agents in a single task. The model is trained — not just prompted — to decompose long-horizon work into a fan-out of parallel sub-tasks, run them concurrently, and reconcile the results. On BrowseComp, plain K2.6 scores 83.2%; with agent swarms enabled, the same model scores 86.3% on the same benchmark.

Claw Groups extend this to heterogeneous agents with persistent memory: a planner-agent, a researcher-agent, a coder-agent, and a verifier-agent can each maintain their own scratchpad across the full session, share intermediate state through a structured memory protocol, and pick up where they left off after a tool failure. The closest closed-weights analogue is Anthropic’s task-budgets beta, but Moonshot’s primitive is more permissive — it lets the agents themselves decide when to fan out.

Long-horizon coding

Moonshot’s official disclosures put continuous coding at up to 13 hours and 4,000+ tool calls in a single session. The published reference worklog shows K2.6 running a 5-day proactive operation autonomously, with a heterogeneous Claw Group of planner, researcher, coder, and verifier agents picking up across tool failures and resumes. This is the workload class K2.6 was specifically post-trained for, and the one where it most clearly outperforms K2.5.

API basics: thinking modes, multimodal, tool use

You call K2.6 with the model id kimi-k2.6 against the Moonshot platform API at https://api.platform.moonshot.ai/v1. The API is OpenAI-compatible, which means existing OpenAI SDKs work out of the box.

• Thinking mode (default): The model emits a hidden reasoning trace before the user-visible answer. Reasoning is returned in response.choices[0].message.reasoning; the visible answer is in .content as usual. Recommended temperature: 1.0, top-p: 0.95, max-tokens: up to 98,304 for hard reasoning.
• Instant mode: Disable thinking with extra_body={"thinking": {"type": "disabled"}} for low-latency chat. Recommended temperature drops to 0.6.
• Preserve thinking: Set thinking: {type: "enabled", keep: "all"} to retain the full reasoning trace across multi-turn conversations. Useful for agentic loops; doubles the output token bill.
• Multimodal: Pass images as base64 PNG/JPG via image_url; pass MP4 video via video_url (official API only — vLLM and SGLang do not yet support video).
• Tool use: Standard OpenAI tool-call schema. K2.6 hits 96.6% tool-invocation success across the Moonshot tool benchmark, the highest of any model with public weights.

A minimal example in Python:

import openai

client = openai.OpenAI(
    api_key="your-key",
    base_url="https://api.platform.moonshot.ai/v1",
)

messages = [
    {"role": "system", "content": "You are Kimi, an AI assistant created by Moonshot AI."},
    {"role": "user", "content": [
        {"type": "text", "text": "Refactor this function for readability and add tests."}
    ]},
]

response = client.chat.completions.create(
    model="kimi-k2.6",
    messages=messages,
    max_tokens=4096,
)

print("Reasoning:", response.choices[0].message.reasoning)
print("Answer:",   response.choices[0].message.content)

For migration patterns from earlier Moonshot or open-weights models, see our guide on self-hosting LLMs in 2026, which covers the inference-server side you will reuse with K2.6.

Benchmarks: what the numbers actually say

Benchmarks are useful for narrowing your shortlist, not for picking a winner. The table below is the current snapshot for K2.6 against the three models it gets compared to most often: Claude Opus 4.7, GPT-5.5, and DeepSeek V4 Pro.

The honest read: K2.6 is the best model in the world right now for the specific shape of work that is “run unsupervised for hours, fan out into sub-agents, finish a real coding task.” It is also the best open-weights model on every coding benchmark we tracked except Terminal-Bench 2.0. Opus 4.7 still wins SWE-bench Verified and graduate-science questions; GPT-5.5 still wins terminal-style breadth; DeepSeek V4 Pro is still cheaper on raw output cost. K2.6 is the model you reach for when you want closed-weights quality at open-weights economics.

For more on the open-source side, see our DeepSeek V4 complete guide and the 2026 open-source LLM landscape.

Pricing and cost of ownership

Moonshot publishes hosted-API pricing on platform.moonshot.ai. Open-weights deployment costs depend on your hardware. Both numbers matter; the right answer is usually a hybrid.

Cache reads on Moonshot’s API are 83% off the input rate, which is unusually aggressive — comparable to OpenAI’s and Anthropic’s prompt-caching discounts but applied automatically rather than via explicit cache markers. The blended rate at a 3:1 input:output ratio is $1.71 per million tokens, roughly 5–6x cheaper than Opus 4.7 and 3x cheaper than GPT-5.5. DeepSeek V4 Pro at base pricing is roughly 17% cheaper still on output; during the active 75%-off promo (through May 31, 2026) it is 4–5x cheaper than K2.6 — a meaningful number to factor into any short-term migration decision.

Real-world cost: a coding-agent workload

Take the same workload we used in our Opus 4.7 guide: an autonomous coding agent fixing 100 medium-complexity bugs per day, with 50,000 cached context tokens, 5,000 fresh input tokens per task, and 8,000 output tokens per task.

• K2.6 first-task cost: 50K input @ $0.95/M = $0.0475, plus 5K @ $0.95/M = $0.005, plus 8K output @ $4/M = $0.032. Total: ~$0.085.
• K2.6 subsequent task (cache warm): 50K cache read @ $0.16/M = $0.008, plus 5K input @ $0.95/M = $0.005, plus 8K output @ $4/M = $0.032. Total: ~$0.045.
• K2.6 daily total (100 tasks): $0.085 + 99 × $0.045 = ~$4.50/day, or roughly $135/month.
• Same workload on Opus 4.7: ~$750/month — about 5.5x more expensive.
• Same workload on DeepSeek V4 Pro (base): ~$113/month — about 17% cheaper than K2.6, with a measurable quality drop on long-horizon agentic tasks (K2.6’s post-trained specialty), but a clear lead on competitive programming (LiveCodeBench 93.5 vs 89.6).
• Same workload on DeepSeek V4 Pro (75% promo, through May 31, 2026): ~$28/month — roughly 5x cheaper than K2.6 while the promo lasts. If your eval window fits inside that promo, V4 Pro is hard to beat on raw cost.
• Self-hosted K2.6 on 8x H200: at on-demand cloud rates (~$3/GPU-hour), roughly $580/month at 60% utilisation, before storage and egress. Pays off vs the hosted API only at very high throughput, or when data residency / open-weights are hard requirements.

The economic case for K2.6 is strongest in two scenarios: (1) high-volume coding agents where the workload is too expensive on Opus and not quite right for DeepSeek, and (2) regulated or air-gapped environments where you need open weights you can audit and self-host. For most teams, start on the hosted API, port to self-hosted only if economics or compliance demand it.

Self-hosting K2.6

K2.6 is open-weights under a modified MIT license. Both code and weights are on Hugging Face at moonshotai/Kimi-K2.6. Native INT4 quantisation is published by Moonshot directly; community quants for llama.cpp, LM Studio, Jan, and Ollama landed within 48 hours of release.

Realistic deployment options:

• vLLM: Production default. Tensor-parallel across 8x H100 80GB or 4x H200 141GB for full 256K context. Single H100 serves the INT4 quant at ~32K context. --model moonshotai/Kimi-K2.6 --tensor-parallel-size 8 --max-model-len 262144 --enable-prefix-caching.
• SGLang: Faster on agentic workloads with shared-prefix patterns; the Claw Groups primitive specifically benefits from SGLang’s RadixAttention. Same 8x H100 hardware target.
• KTransformers: CPU+GPU hybrid for offline / single-machine inference. Runs the full FP16 weights on a workstation with 1.5TB system RAM and one H100 at acceptable interactive latency.
• llama.cpp / Ollama: Quantised to INT4 or lower for local laptop / Mac Studio inference. Real, but slow (5–15 tok/s on M3 Ultra) and limited to ~32K context. Useful for development, not production.

Hardware planning is the same shape as DeepSeek V3 — both K2.6 and V3 use MLA attention. DeepSeek V4 (released April 24, 2026, four days after K2.6) switched to a CSA+HCA hybrid attention scheme that buys a 1M context with much smaller KV-cache, so V4 deployments are not a drop-in template for K2.6. The main K2.6 operational delta vs a chat workload is the agent-swarm primitive: long-running sessions hold KV-cache state for hours, so plan for 30–50% higher steady-state KV memory than a chat-only deployment.

For a deeper walk-through of the open-weights operations side, see our self-hosting LLMs guide.

Agent Swarms: the headline capability, in detail

The single biggest reason to care about K2.6 specifically — as opposed to “another open-weights MoE” — is the Agent Swarms primitive. Most agent frameworks today bolt orchestration on top of the model: LangGraph, CrewAI, AutoGen all do this in user-space. K2.6 internalises the primitive: the model has been post-trained to decide when to fan out, how many sub-agents to spawn, what each one does, and how to reconcile results.

What that buys you, concretely:

1. Quality lift on parallelisable work. BrowseComp jumps from 83.2% to 86.3% with swarms enabled. The lift is bigger on tasks with naturally parallel structure — large literature reviews, multi-repo refactors, batch data validation — and smaller on linear tasks.
2. Latency reduction at fixed quality. A 20-minute single-agent run can drop to 3–5 minutes when the work decomposes into 20 parallel sub-tasks. The token bill is roughly the same; wall-clock is dramatically lower.
3. Cost discipline. Sub-agents inherit the parent’s task budget. They cannot silently spawn an exponential tree of grandchildren. Moonshot’s reference scheduler caps each task at 300 active agents and 4,000 total steps; you can lower these in your client.
4. Recovery from tool failures. A failed sub-agent does not halt the parent. The parent receives a structured failure report and decides whether to retry, replan, or proceed without that result. K2.6’s 96.6% tool-invocation success rate matters here — the remaining 3.4% is handled by the swarm, not by the human.

The honest caveat: swarms shine on parallelisable work and add overhead on inherently sequential work. A bug fix that requires reading file A, then file B, then file C, then writing a patch is not a swarm problem. A bug fix that requires reading 80 files in a monorepo to understand a regression — that is a swarm problem.

When to use K2.6 vs Opus 4.7 vs GPT-5.5 vs DeepSeek V4

The most expensive mistake in 2026 is treating “the best model” as a single answer. The cheapest router that pays for itself in a week is one that knows which workload goes to which model.

Known limitations

Moonshot’s K2.6 launch post is, by frontier-lab standards, fairly candid. The honest picture from third-party reviews and our own testing:

1. Output speed is mid-pack. 34.4 tokens per second on the Moonshot API, ranked #46 of 83 models tracked by Artificial Analysis. Time-to-first-token is 3.04 seconds, meaningfully slower than Opus 4.7 (~1.2s) or GPT-5.5 (~0.8s). Thinking mode is the main cause; instant mode roughly doubles the throughput.
2. Terminal-Bench gap is real. 66.7% on Terminal-Bench 2.0 is a clear loss to GPT-5.5 (82.7%). If your agents live in a shell rather than in code, K2.6 is not the right model.
3. SWE-bench Verified still trails Opus 4.7. 80.2% vs 87.6%. On the hardest single-file bug fixes, Opus 4.7 still wins. K2.6 closes the gap on multi-file and multi-language work, but Verified is still Anthropic’s home turf.
4. Self-hosted INT4 has a transformers pin. transformers>=4.57.1, <5.0.0. Older inference stacks silently fall back to FP16 and OOM. This will catch you off guard the first time.
5. Verbosity is high. 170M output tokens during the Artificial Analysis Intelligence Index evaluation — among the chattiest models tested. Use instant mode or aggressive max-tokens caps for cost-sensitive workloads.
6. Agent Swarms add overhead on sequential work. Tasks that do not parallelise see 10–25% latency overhead from the swarm scheduler. Disable swarms (one extra-body flag) when you know the task is linear.
7. Vision encoder is good, not great on highly stylised content. MoonViT matches Opus 4.7 on dense documents and screenshots, but lags on artistic / illustrative imagery. Not a typical agent-coding concern, but worth knowing.
8. Hosted API rate limits are conservative for now. Default tier is 50 RPM; bumping to production tiers requires a request to support@moonshot.ai. Plan ahead if you intend to ship K2.6 to production traffic on day one.

Comparing K2.6 to Opus 4.7, GPT-5.5, and DeepSeek V4 Pro

The 2026 frontier is now a four-way race rather than a three-way one.

vs Claude Opus 4.7. Opus 4.7 wins SWE-bench Verified (87.6% vs 80.2%), MCP-Atlas (77.3% vs ~74%), and graduate-level science (GPQA Diamond 94.2% vs 90.5%). K2.6 wins on cost (5–6x cheaper), open weights, agent swarms, web research, multilingual coding, and competitive programming. Pick Opus 4.7 when the bottleneck is “can it ship this single PR.” Pick K2.6 when the bottleneck is “can it run unsupervised for hours, coordinate sub-agents, and finish a multi-task.”

vs GPT-5.5. GPT-5.5 wins on overall intelligence index (60 vs 54), Terminal-Bench 2.0 (82.7% vs 66.7%), and TTFT latency. K2.6 ties on SWE-bench Pro, leads on web research and competition math, and is roughly 3x cheaper. Pick GPT-5.5 when terminal/agent breadth dominates. Pick K2.6 when the agentic workload is structured, parallelisable, and cost-sensitive.

vs DeepSeek V4 Pro. DeepSeek V4 Pro shipped four days after K2.6 (April 24, 2026) and is the closest peer. The two trade wins: K2.6 leads on agentic tooling (HLE-with-tools, BrowseComp with swarms, DeepSearchQA, multilingual SWE), V4 Pro leads on competitive programming (LiveCodeBench 93.5 vs 89.6) and ties on SWE-bench Verified (80.6 vs 80.2). On raw cost, V4 Pro at base pricing is ~17% cheaper on output; during the 75%-off promo through May 31, 2026 it is 4–5x cheaper. The differentiation is concentrated in long-horizon agentic work: K2.6’s post-training specifically targets it, V4’s does not. For 12+ hour autonomous coding sessions, K2.6 is the clear win; for everything else, the call comes down to license, context length (V4 has 1M, K2.6 has 256K), and price-window. For the deeper trade-off, see our Kimi K2.6 vs DeepSeek V4 head-to-head.

FAQ

What is the model id for Kimi K2.6 in the API?

kimi-k2.6. Use it as the model parameter against https://api.platform.moonshot.ai/v1. The API is OpenAI-compatible.

What license is K2.6 released under?

A modified MIT license. Both the code repository and the model weights are released under the same license. Commercial use is permitted; redistribution must preserve the license file.

What hardware do I need to self-host K2.6?

Full 256K context at FP16 / BF16 needs 8x H100 80GB or 4x H200 141GB. The native INT4 quant runs on a single H100 80GB at ~32K context. CPU+GPU hybrid via KTransformers needs ~1.5TB system RAM and one H100. Local Mac Studio inference via llama.cpp works at INT4, ~5–15 tok/s.

How much does the Moonshot hosted API cost?

$0.95 / $4.00 per million tokens (input / output). Cache reads are $0.16 per million — an 83% discount on input. Blended rate at a 3:1 input:output ratio is $1.71 per million.

Does K2.6 support prompt caching?

Yes, automatically. Repeated prefixes are cached and billed at $0.16 per million on read. Unlike Anthropic’s API, you do not need to mark cache control points explicitly.

Is K2.6 better than Claude Opus 4.7?

For long-horizon autonomous coding, web research, multilingual coding, and competition math — yes. For SWE-bench Verified single-file bug fixes, graduate-level science, and dense MCP-tool workflows — Opus 4.7 still leads. On cost, K2.6 wins by 5–6x.

Is K2.6 better than GPT-5.5?

For coding (SWE-bench Pro tied at 58.6%), web research (DeepSearchQA 92.5% vs ~80%), and competition math (AIME 2026 96.4% vs ~94%), yes. For overall intelligence index (54 vs 60) and terminal/agent breadth (66.7% vs 82.7% on Terminal-Bench 2.0), GPT-5.5 still leads. K2.6 is also ~3x cheaper.

Is K2.6 better than DeepSeek V4 Pro?

On long-horizon agentic work, agent swarms, multilingual SWE, and tool-use success rate — yes, decisively. On competitive programming (LiveCodeBench), V4 Pro leads (93.5 vs 89.6). On SWE-bench Verified — they’re tied (80.6 vs 80.2). On raw output cost — V4 Pro is ~17% cheaper at base pricing and 4–5x cheaper during its 75%-off promo through May 31, 2026.

What are Agent Swarms and when should I use them?

A native K2.6 primitive that lets the model spawn up to 300 sub-agents and coordinate up to 4,000 steps per task. Use them when work parallelises naturally — large literature reviews, multi-repo refactors, batch validation. Disable them on linear tasks (a single bug fix, a single chat turn) where they add 10–25% latency overhead with no quality gain.

Does K2.6 support vision and video?

Yes. Images via base64 PNG/JPG (image_url) and MP4 video via base64 (video_url). Video input is currently official-API-only; vLLM and SGLang do not yet support it.

Does K2.6 work with Cursor and Windsurf?

Partly. As of early May 2026, Cursor has an open community request to add K2.6 to its model picker but no first-class integration — you can use it via OpenRouter or a custom OpenAI-compatible endpoint. Windsurf supports the K2 family in its picker; K2.6 specifically is rolling out. The wiring is identical to any other OpenAI-compatible endpoint either way. See our guide on Cursor IDE for setup patterns.

How do I disable the thinking trace for low-latency chat?

Pass extra_body={"thinking": {"type": "disabled"}} on the official API, or extra_body={"chat_template_kwargs": {"thinking": False}} on vLLM / SGLang. Drop temperature to 0.6 in instant mode for best results.

Will K2.6 replace human engineers?

No. It will replace engineers who do not use it. The bottleneck for shipping software is still architecture, code review, judgement on tradeoffs, and accountability for production. K2.6 is a force multiplier on a senior engineer with strong agent-design instincts; it is not a substitute for one.

Next steps

If you are deciding where K2.6 fits in your stack, the cheapest experiment is also the most informative: pick one workflow, route it to K2.6 for a week, and measure. If the workflow is “run unsupervised for hours, coordinate sub-agents, and finish complex coding work,” you also need engineers who can wire it up properly — agent swarms, MCP tools, evals, the lot.

Hire a Codersera-vetted Python or AI engineer to integrate Kimi K2.6 into your codebase, build the routing layer that sends the right task to the right model, and stand up the evals that tell you whether it is actually working. Vetted, remote-ready, and available in days — not months.