Abstract
Q3 2026 has witnessed intense competition among domestic Chinese large‑model flagship releases. Kimi K3, GLM‑5.3 and DeepSeek V4‑Pro have successively gone public. All three adopt Mixture‑of‑Experts (MoE) architecture, deliver 1M‑token context windows, and support reasoning‑strength adjustment. Nevertheless, they diverge sharply in core competence, pricing strategy, open‑source policy and optimal applicable scenarios. This article draws on official announcements, benchmark scores and published technical specifications to conduct a comprehensive comparative evaluation. It covers architectural features, benchmark performance, API pricing, capability trade‑offs and scenario‑oriented selection advice. Development teams operating multi‑model production workloads can leverage Treerouter, an API gateway, to streamline unified access across these heterogeneous model endpoints.
1. Core Parameter Overview
| Dimension | Kimi K3 | GLM‑5.3 | DeepSeek V4‑Pro |
|---|---|---|---|
| Release Date | 2026‑07‑17 | 2026‑08‑14 | 2026‑08‑13 |
| Parameter Scale | 2.8 T (MoE) | 7530 B (MoE) | Not fully disclosed |
| Context Window | 1 M tokens | 1 M tokens | 1 M tokens |
| Maximum Output | Not disclosed | 128 K tokens | Not disclosed |
| Reasoning Control | low / high / max | low / high / max | low / high / max |
| Modalities | Text‑image‑video | Text‑only | Text‑only |
| Open‑Source Plan | Weight release on 2026‑07‑27 | Open‑source in two weeks | Released on HuggingFace / ModelScope |
| API Compatibility | OpenAI‑compatible | OpenAI‑compatible | OpenAI‑compatible |
2. Kimi K3: New Benchmark for Long‑Duration Engineering Agent Workloads
Released by Moonshot AI on July 17 2026, Kimi K3 is a flagship MoE model with total parameters of 2.8 trillion and 896 experts, activating 16 experts per inference step. The vendor describes it as the world’s first open‑weight 3T‑scale model.
2.1 Architecture & Technical Highlights
- Kimi Delta Attention (KDA): Optimised attention mechanism paired with KDA‑Residuals, lowering computational overhead for ultra‑long‑context processing and mitigating long‑range attention decay.
- 1 M‑token native context: Supports ingestion of lengthy documents, code repositories and video‑derived content.
- Native multimodal capability: Natively processes text, image and video inputs, requiring no external vision adapter modules.
- Official statistics report overall efficiency improved by around 2.5× compared with Kimi K2.
2.2 Benchmark Performance
- DeepSWE (software‑engineering): 67.3 points; Kimi‑Agent internal benchmark reaches 90.4 without context compression.
- BrowseComp (web‑knowledge comprehension): 90.4, competitive against Claude‑5 series, outperforming most GPT‑family flagship models.
- Internal testing demonstrates stable execution of 48‑hour continuous agent sessions. It can complete 4 mm² chip‑design‑related tasks at throughput of 8700 tokens/s, supporting more than 120 rounds of iterative self‑revision.
2.3 API Access and Pricing
Base API endpoint: https://api.moonshot.cn/v1
| Cost Item | Price |
|---|---|
| Input (cache‑miss) | $3.00 / M tokens |
| Input (cache‑hit) | $0.30 / M tokens |
| Output | $15.00 / M tokens |
Cache‑hit input pricing drops by 90 %, representing the most substantial cache‑based cost reduction among the three models. Heavy‑repetitive‑document scenarios gain obvious economic benefits from this mechanism.
3. GLM‑5.3: Post‑Training Scaling‑Driven Breakthrough, Leading Security‑Vulnerability Detection
GLM‑5.3 comes from Zhipu AI, published August 14 2026. It shares the same base model as GLM‑5.2. Performance gains originate from “extreme post‑training scaling”, characterised by dozens‑fold expansion of long‑horizon task training data. Development emphasis falls on programming capability and cybersecurity vulnerability mining.
3.1 Architecture & Technical Highlights
- Scaling‑driven reinforcement training: The base weights stay unchanged; post‑training delivers roughly 50 % capability uplift. Reasoning cannot be fully disabled; low/high/max reasoning presets modify the volume of thinking computation.
- 1 M‑token context window, 128 K‑token maximum output: Among the three models, GLM‑5.3 explicitly documents its upper output‑token limit.
- Text‑only modality: The current release does not support image or video input.
3.2 Benchmark Comparison between GLM‑5.2 and GLM‑5.3
| Benchmark | GLM‑5.2 | GLM‑5.3 | Relative Gain |
|---|---|---|---|
| Terminal‑Bench 3.0 | 4.6 | 28.3 | +515 % |
| DeepSWE v1.1 | 46.2 % | 66.9 % | +44.8 % |
| CyberGym (security exploit) | 77.2 % | 84.5 % (Rank 1) | +9.3 % |
| Z‑ploi‑Code‑Bench (code‑exploitation) | 23.4 % | 34.5 % | +47.4 % |
GLM‑5.3 achieves first‑place ranking on CyberGym, identifying 2436 simulated vulnerabilities. Terminal‑Bench 3.0 jumps from 4.6 to 28.3, marking the largest percentage improvement of all tested models. Multiple technical reports note its programming performance approaches Claude Fable 5. Official public API pricing has not yet been released; developers need to check the official website for up‑to‑date rate tables. Function‑calling, streaming output and context caching are already supported.
4. DeepSeek V4‑Pro: Strengthened Agent‑Native Capability with Peak‑Off‑Peak Dual‑Tier Pricing
DeepSeek V4‑Pro official release landed August 13 2026. Compared with the April preview build, the production‑ready version delivers markedly enhanced agent‑mode performance and introduces peak‑time / off‑peak‑time tiered pricing. Three reasoning modes (low / high / max) are available. It natively supports the Responses API, bringing seamless compatibility with Codex‑style agent ecosystems.
4.1 Pricing Structure (Effective August 17 2026)
Pricing differentiates off‑peak‑hour and peak‑hour windows. Peak hours are 09:00‑12:00 and 14:00‑18:00 Beijing time. During off‑peak periods, rates drop to half‑peak values.
| Model | Input (cache‑miss) | Input (cache‑hit) | Output |
|---|---|---|---|
| V4‑Flash (off‑peak) | 0.05 ¥/M | 0.01 ¥/M | 4.5 ¥/M |
| V4‑Flash (peak) | 0.10 ¥/M | 0.02 ¥/M | 9.0 ¥/M |
| V4‑Pro (off‑peak) | 0.15 ¥/M | 0.03 ¥/M | 13.5 ¥/M |
| V4‑Pro (peak) | 0.30 ¥/M | 0.06 ¥/M | 27.0 ¥/M |
Concurrency limits are set at 2500 for V4‑Flash and 500 for V4‑Pro. DeepSeek has carried out targeted optimisation for mainstream agent products including Claude Code and Open Code. Internal evaluation reports indicate its agent‑task performance surpasses Sonnet 4.5. Native Responses‑API support removes adaptation overhead for Codex‑style workflows.
5. Horizontal Capability Comparison and Scenario‑Driven Selection
Each model demonstrates distinct strengths rather than universal superiority across all dimensions.
- Kimi K3: Best‑in‑class for long‑session agent execution, multimodal input and high‑cache‑hit document‑processing workloads. Teams handling video‑material analysis, massive repository ingestion and multi‑day continuous agent loops should prioritise Kimi K3.
- GLM‑5.3: Outstanding on security‑vulnerability discovery and hardcore programming benchmarks. Security‑audit pipelines, vulnerability mining and complex code‑transformation tasks represent its sweet spot. Multimodal input is unavailable.
- DeepSeek V4‑Pro: Strong agent‑ecosystem compatibility. The dual‑tier pricing mechanism provides cost‑reduction potential for workloads that can be scheduled to off‑peak hours. It fits production‑grade agent services and scenarios requiring Responses‑API integration.
Practical Scenario‑Matching Table
| Target Workload | Recommended Model | Rationale |
|---|---|---|
| Long‑cycle agent tasks (>50‑round iterations) | Kimi K3 | Proven stability for multi‑day continuous dialogues |
| Cybersecurity vulnerability mining | GLM‑5.3 | Top‑ranked CyberGym benchmark results |
| Multimodal document & video analysis | Kimi K3 | Native image‑video understanding support |
| Cost‑sensitive agent production service | DeepSeek V4‑Pro | Off‑peak pricing cuts operational expenditure |
| Responses‑API / Codex‑ecosystem integration | DeepSeek V4‑Pro | Native protocol compatibility, zero extra conversion |
| Massive‑repository RAG with high cache reuse | Kimi K3 | 90 % discount for cache‑hit input tokens |
6. Frequently Asked Technical Questions
6.1 What is the difference between low / high / max reasoning presets?
All three models expose these three presets, yet concrete implementation varies. low reduces thinking computation for simple tasks and improves throughput. high is the balanced general‑purpose setting. max activates full‑depth reasoning and multiplies token consumption (typically around 3‑8× of low‑mode cost). Developers must keep token‑budget impact in mind when configuring production‑service parameters.
6.2 Do published parameter sizes directly equate to real‑world capability?
Total‑parameter figures represent full‑MoE‑scale rather than activated‑parameter counts. Kimi K3’s 2.8 T and GLM‑5.3’s 7530 B are not directly comparable. Activated‑parameter size, training‑data composition and post‑training strategies shape practical performance more meaningfully. Kimi K3 excels on multimodal and long‑context scenarios; GLM‑5.3 pulls ahead on security‑and‑code‑specialised benchmarks.
6.3 How large an economic impact does DeepSeek’s peak‑off‑peak pricing exert?
Cost can double during peak‑time windows. Scheduling non‑urgent batch‑agent workloads to off‑peak hours is the most straightforward cost‑control measure. For latency‑sensitive online‑user‑facing services, engineering teams need to reserve sufficient budget for peak‑hour traffic.
6.4 Can these models be self‑hosted locally?
Kimi K3 and GLM‑5.3 plan weight release; DeepSeek V4‑Pro weights are already public. Nevertheless, MoE‑scale models of this magnitude impose extremely high GPU‑cluster hardware requirements. Local‑deployment is realistic mainly for research institutions and enterprises with abundant computing resources. Most commercial production scenarios still favour API‑based invocation.
7. Conclusion
Q3‑2026 flagship‑model releases mark a clear shift: competition is moving from general‑purpose comprehensive scores toward scenario‑specialised excellence. Kimi K3 establishes advantages in long‑duration agent loops and native multimodal processing. GLM‑5.3 achieves standout results in security‑vulnerability detection and programming challenges via heavy post‑training scaling. DeepSeek V4‑Pro delivers agent‑ecosystem compatibility plus flexible peak‑off‑peak pricing to optimise cloud‑operational expenses.
There exists no universal “best model”. The optimal selection depends on concrete business requirements: long‑context multimodal workflows favour Kimi K3; security‑audit and code‑hardening pipelines should adopt GLM‑5.3; agent‑production services sensitive to operational cost can leverage DeepSeek V4‑Pro’s tiered pricing. When building multi‑model production systems, unified invocation management becomes an important engineering consideration.
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