Qwen 3.7 Max: With a parameter of 2.4 trillion, over Kimi K3？

TLDR: Alibaba’s Qwen3.8-Max (preview, 2.4 trillion parameters, multimodal with image/video/document input) goes head-to-head with Moonshot AI’s Kimi K3 (2.8T parameters, open weights soon, 1M context). Qwen3.8-Max excels in multimodal and productivity tasks with preview pricing at ~10% of standard rates, while Kimi K3 leads in some open benchmarks and raw openness.

Both are frontier models accessible today via APIs. For reliable, cost-effective integration, CometAPI offers seamless Qwen access with OpenAI-compatible endpoints, competitive pricing, and easy scaling for developers and businesses.

Key Takeaways:

• Scale & Capabilities: From x's post, Qwen3.8-Max brings native multimodal (images, video, docs) to a 2.4T-parameter model; Qwen3.7-Max shines in agentic coding and long-horizon tasks.
• Pricing & Access: Qwen3.8-Max has Competitive API rates (~$2.50/$7.50 per 1M input/output, often discounted); preview Qwen 3.8 at reduced rates via Token Plan/Qoder.
• Vs. Kimi K3: Kimi K3 (2.8T, open weights July 27, 2026) often edges benchmarks but Qwen offers stronger multimodal and ecosystem integration; Qwen is cheaper in many API scenarios.
• Access Today: Preview on Alibaba Cloud/Token Plan/Qoder at discounted rates; OpenAI/Anthropic API compatible.
• Practical Wins: Superior for full-stack dev, office automation, video analysis, and agent workflows.

What Is Qwen 3.8 Max?

Qwen 3.8 Max, officially previewed as Qwen3.8-Max-Preview, is Alibaba’s Qwen team’s latest flagship large language model (LLM). Announced at the World AI Conference in Shanghai around July 19, 2026, it represents a massive leap in scale from the Qwen 3.7 series.

Core Specifications

• Parameters: 2.4 trillion total — making it one of the largest publicly discussed models, second only to Moonshot AI’s Kimi K3 at ~2.8T.
• Architecture: Sparse Mixture-of-Experts (MoE). Only a subset of parameters activates per token, balancing power with efficiency (exact active parameter count not yet disclosed — a key detail for inference costs).
• Multimodal Capabilities: Processes text, images, videos, and documents natively. This is Qwen’s first model exceeding 1 trillion parameters with strong multimodal support.
• Context Window: Up to 1 million tokens, inherited and refined from Qwen 3.7 Max.
• Variants: Flagship 2.4T Max plus a smaller ~27B variant for lighter use cases.

How Qwen 3.8 Max Works: Innovations and Technical Edge

Qwen3.8-Max leverages decades of Alibaba’s scaling expertise:

1. Massive Scale with Efficiency: 2.4T parameters via MoE – only a subset activates per token, balancing power and inference cost (similar to prior Qwen MoE like 235B-A22B).
2. Hybrid Reasoning: Inherits “Thinking/Non-Thinking” modes for flexible control over depth, speed, and cost. Perfect for agentic tasks requiring multi-step planning.
3. Multimodal Integration: Unified processing of text + vision + video. Enables richer applications like document analysis with images or video-based reasoning.
4. Long-Context and Agentic Focus: Optimized for long-horizon tasks (e.g., full app cloning, complex workflows). Supports advanced tool use, RAG, and function calling.
5. Training Advances: Built on vast multilingual data (119+ languages), improved positional embeddings, and techniques reducing hallucinations while boosting STEM/coding.

How It Works in Practice:

• Input → Tokenizer + embeddings → MoE router selects experts → Transformer layers process → Output generation.
• Supports agentic coding (e.g., full-stack projects), data analysis pipelines, and office automation.

Community Reddit tests show promising raw power in coding and complex queries, though some note occasional thinking loops in early preview.

For developers: Pair with tools like CometAPI for reliable, unified access across providers, reducing latency and costs while testing Qwen alongside Claude, GPT, etc.

Pricing and Access: What You Can Actually Use Today

Preview Availability (as of July 2026):

• Alibaba Token Plan: Credit-based subscription.
  • Lite: $6 for 2,500 credits/week.
  • Pro: $68 for 40,000 credits/week (supports 6-8 concurrent agents).
• Discount: 10% of standard pricing during preview.
• Platforms: Qwen Chat, Alibaba Cloud, Qoder (coding), QoderWork (productivity).
• API: OpenAI/Anthropic compatible. No standalone per-token rates published yet for 3.8-Max (predecessor: ~$1.25/M input, $3.75/M output).

Open-Weights: Promised “soon” — huge for self-hosting/fine-tuning, though hardware demands are extreme (~1.2TB at 4-bit).

CometAPI Recommendation: For production, skip direct vendor management. CometAPI provides one API key for 500+ models, including Qwen series. Unified billing, endpoints, and reliability — ideal for A/B testing Qwen 3.8 Max vs. others without code rewrites. Perfect for Cometapi.com users building AI apps. Sign up at cometapi.com for seamless integration.

This makes Qwen accessible without Alibaba Cloud commitment, great for startups and global devs.

Does Qwen3.8 Max Beat Kimi K3?

On parameter count: no

If the question is "does Qwen3.8 Max have more parameters than Kimi K3?", the answer is straightforward: no. Qwen3.8-Max-Preview is reported by Qoder as a 2.4T-parameter model. Kimi K3 is documented by Moonshot as a 2.8T-parameter model. On total parameters alone, Kimi K3 is larger by 400 billion parameters.

That does not automatically make Kimi K3 better at every task. Parameter count is not a scoreboard by itself. Training data, architecture, expert routing, active parameters per token, post-training, inference stack, tool harnesses, and context management all influence real performance. A smaller model can beat a larger one on some tasks if it has better data, better post-training, better tools, or better inference-time reasoning.

On public evidence: Kimi K3 is ahead right now

Kimi K3 currently has the better public evidence package. Moonshot's Kimi K3 guide and technical blog disclose architecture, context, vision, expert activation, API limits, and release plans. OpenLM's Kimi K3 page mirrors a detailed benchmark table across coding, agentic work, reasoning, knowledge work, and vision. Kimi K3's Frontend Code Arena result at 1,679 points and summarized the model's 1M context, 16/896 expert activation, and pricing signals.

Qwen3.8-Max-Preview is very important, but the preview is less documented. The strongest current sources are Qoder's launch page and Alibaba Cloud Model Studio's OpenCode configuration. Those are valuable, but they do not yet provide the same level of benchmark detail, active-parameter disclosure, architecture explanation, license terms, or CometAPI production route detail that Kimi K3 has.

Key Differences:

• Openness: Kimi K3 wins with imminent full open weights for self-hosting/fine-tuning. Qwen3.8-Max preview now, open soon.
• Multimodal: Both strong; Qwen3.8 emphasizes video/docs integration.
• Performance: Kimi K3 often leads raw benchmarks (e.g., GPQA, coding arenas); Qwen competitive or superior in agent/productivity scaffolds and specific tasks like spreadsheets/kernel opt. Qwen cheaper in API use (e.g., 3x in some comparisons).
• Context & Speed: Kimi 1M context; Qwen strong long-context (262K+ in prior). Qwen often faster output.
• Use Cases: Qwen for integrated Alibaba ecosystem, office automation, multimodal enterprise. Kimi for open-source enthusiasts and max customization.

Conclusion: Should You Adopt Qwen 3.8 Max?

Yes for experimentation and coding/multimodal needs — especially via accessible platforms like CometAPI. It’s not yet a full replacement for closed frontiers but a powerful, affordable option pushing the ecosystem forward. Stay tuned for benchmarks and full release.