Kimi K3 vs GLM-5.3 vs DeepSeek-V4: Architecture, Benchmarks and Deployment

Posted on Oct 21 • Originally published at blog.llmgo.top

This guide compares Kimi K3, GLM-5.3, and DeepSeek-V4-Pro using model cards. Reported benchmarks are not this site's controlled measurements. Workload sections describe evaluation plans rather than completed experiments. See the GPT / Claude / Gemini comparison for commercial model considerations.

Sources: Kimi K3, GLM-5.3, DeepSeek-V4-Pro.

K3 combines 69 KDA layers with 24 Gated MLA layers. Constant recurrent state in KDA does not make the complete model's cache constant. DeepSeek-V4 uses Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA), and its configuration includes shared experts. See the attention architecture guide.

GLM-5.3 reuses the GLM-5.2 base model and attributes its gains to post-training. This does not establish an exact sandbox count, training interaction count, or universal troubleshooting success rate.

The following values come from the comparison in GLM-5.3's model card. They describe that report, not an independent reproduction or universal ranking.

Read the official table and footnotes for harness, reasoning, context, and time budgets. Its Terminal-Bench 3.0 result uses a specified Claude Code harness and avg@3. An absent result is neither zero nor evidence that a model has never been evaluated. Pass@1, Pass@k, and different harness results cannot be merged into one 80.6%–96.4% score range. See LLM evaluation.

Freeze authorized source files and token budgets. Request field values, page references, and supporting excerpts. Measure numeric accuracy, citation accuracy, unsupported claims, latency, and cost. Needle retrieval alone does not establish financial-audit reliability.

Reset identical faulty images before each run. Fix tool permissions, turns, and deadlines. Use deterministic probes to verify DNS, Service, and gRPC recovery; record unnecessary changes, unsafe actions, and rollback behavior.

Pin the repository commit, issue, and hidden tests. Record patches, commands, pass rates, and repeated runs under equal tool and compute budgets. Passing tests written by the model itself does not prove task completion.

Filter candidates by input modality and available kernels, then measure quality and SLA. Total weights still determine MoE capacity. DeepSeek's native FP4 / FP8 mix and K3's MXFP4 format require different deployment checks; see the deployment guide.

Record API input, output, cached-input, tool, provider, and time-based charges separately. Throughput depends on request lengths, concurrency, and reasoning budgets; one unqualified TPS number per model is not comparable evidence. Compare useful cost at matched quality, then design retries, circuit breakers, and fallback routing.

No. They are different task versions. Compare matching versions and execution settings.

Its vision and context capabilities make it a candidate. Validate parsing, citations, and numeric extraction on the actual material rather than assuming no hallucinations.

After validating format support, capacity, sustained traffic, quality, and SLA, compare measured costs. The token calculator can assist with arithmetic.

Originally published at LLM Systems Architecture on blog.llmgo.top.