Kimi K3 vs Qwen3.8-Max: Output Length vs Regions

All figures below are from the China AI Hub database, last verified 2026-09-20. Where a field is not publicly disclosed, we say so rather than estimating.

At a glance

Dimension	Kimi K3	Qwen3.8-Max
Status (database)	active	active
Context window	1,048,576 tokens	1,048,576 tokens
Maximum output	1,048,576 tokens	131,072 tokens
Input price (per 1M)	$3.00	$2.00 (Singapore)
Output price (per 1M)	$15.00	$6.00 (Singapore)
Open weights	Yes (Kimi K3 License)	No (proprietary)
API available	Yes	Yes
Reasoning	Yes	Yes
Coding	Yes	Yes
Vision / Video	Yes / Yes	Yes / Yes
Tool calling	Yes	Yes
Structured output	Yes	Yes
Agent capability	Yes	Not listed
Benchmark records in DB	6	5

Pricing

Kimi K3 lists $3.00 input / $15.00 output per 1M tokens; Qwen3.8-Max lists $2.00 input / $6.00 output (Singapore; Beijing/Global list $1.65/$4.951). Qwen is cheaper on both axes, and the output-price gap is roughly 2.5x — significant because K3’s 1M-token output ceiling makes a maximal response a material absolute expense.

Context and output

Both list a 1,048,576-token context window. The output ceilings differ sharply: Kimi K3 lists the full 1,048,576-token maximum output — the only 1M-in/1M-out model in the database — while Qwen3.8-Max lists 131,072. For long-form generation this is the decisive listed difference.

Architecture and parameters

Kimi K3 is a 2.8T-total / 104B-activated MoE (93 layers, 896 experts, KDA + Gated MLA attention). Qwen3.8-Max is a 2.4T-total / 95B-activated MoE (512 experts, 92 layers, Gated DeltaNet + Gated Attention hybrid). Both are frontier-scale sparse architectures using a linear/sparse-attention component for long-context economics — KDA on the Moonshot side, Gated DeltaNet on the Alibaba side. The total-parameter scale is comparable; the activation ratio differs (roughly 3.7% for K3 versus 4.0% for Qwen).

Coding

Both list coding and tool calling. Kimi K3 publishes vendor-reported DeepSWE (67.5) and Terminal-Bench 2.1 (88.3); Qwen3.8-Max publishes Terminal-Bench 2.1 (86.6) and SWE-bench Pro (67.7). Terminal-Bench 2.1 is shared at the same version, which makes the 88.3 vs 86.6 comparison closer than most cross-vendor pairs — though both are vendor-reported under different harnesses.

Vision

Both list vision and video input. On multimodal input the two are equivalent per the database (with K3 carrying the caveat that its modality documentation is internally inconsistent).

Agent capabilities

Both list tool calling and structured output. Kimi K3 additionally lists an explicit agent_capability flag; Qwen3.8-Max lists function calling and web search but not an explicit agent flag. Both are documented agent substrates; the listed difference is K3’s explicit agent flag versus Qwen’s web-search/function-calling surface.

License and openness

This is a clear structural split. Kimi K3 is open weight under the Kimi K3 License (MIT-style with revenue-triggered obligations above $20M / 100M MAU). Qwen3.8-Max is proprietary, closed weight, no self-hosting (the open Qwen3.8-2.4T-A95B is a different, text-only product). Self-hosting is an option in one and not the other.

Deployment

Kimi K3 is self-hostable open weight and served through the Moonshot AI Platform. Qwen3.8-Max is API-only but lists six serving regions (Beijing, Singapore, Hong Kong, Frankfurt, US-Virginia, Tokyo) — the broadest footprint in the database. The deployment trade is self-hosting (Kimi) versus a broad multi-region API (Qwen).

Benchmarks

The database holds 6 benchmark records for Kimi K3 (GPQA Diamond, HLE-Full, DeepSWE, Terminal-Bench 2.1, MMMU-Pro, Video-MME) and 5 for Qwen3.8-Max (Terminal-Bench 2.1, SWE-bench Pro, GPQA Diamond, HLE, MRCR v2 256K). The overlap is partial (Terminal-Bench 2.1, GPQA Diamond, HLE). All are vendor-reported.

Benchmark comparability is limited: even where benchmark names match, the two models’ scores come from different evaluation harnesses and tool setups, and several benchmarks cover only one of the two. Do not rank on headline scores alone.

Why each difference matters

China AI Hub analysis indicates the following per-dimension implications, drawn from the listed facts above.

• Pricing: Qwen3.8-Max has an advantage at a lower input and roughly 2.5x lower output price; the output gap compounds on reasoning and long-generation workloads.
• Context: identical 1M-token input windows; Kimi K3 has an advantage in long-form generation with its full-window 1M-token maximum output versus Qwen’s 131,072.
• Coding: both list coding, tool calling and structured output; Terminal-Bench 2.1 is shared at the same version (88.3 vs 86.6), but both are vendor-reported under different harnesses, so treat the gap cautiously.
• Vision / media: both list vision and video — this axis does not differentiate.
• Agent: both list tool calling and structured output; K3 lists an explicit agent flag, Qwen lists web search and function calling — the surface differs more than the capability.
• License: Kimi K3 has an advantage for self-hosting (open weight, conditional license); Qwen3.8-Max has a limitation here (proprietary, API-only).
• Deployment: Kimi offers self-hosting; Qwen offers a six-region API footprint — choose by which constraint binds.
• Benchmark: K3 records 6 and Qwen records 5 vendor-reported rows with partial overlap; not directly comparable.

Trade-off summary

• Output length: Kimi K3 lists a 1M-token maximum output; Qwen3.8-Max lists 131,072.
• Price: Qwen3.8-Max lists lower input and ~2.5x lower output prices.
• Openness: Kimi K3 is open weight (Kimi K3 License); Qwen3.8-Max is API-only.
• Deployment: Kimi self-hostable; Qwen six-region API.
• Capabilities: closely matched on paper (reasoning, coding, vision/video, tools).

Choose by output length, openness and region: long-form generation and self-hosting favor Kimi K3’s listed capabilities; budget, international multi-region deployment and API-only workflows favor Qwen3.8-Max. Verify current prices on the official pages before committing.

Decision context

China AI Hub analysis indicates the following decision-context implications, drawn from the listed facts above.

For API developers. Qwen3.8-Max lists lower input and ~2.5x lower output prices, which compounds on reasoning and long-generation workloads. Both list tool calling and structured output; K3 lists an explicit agent flag and a 1M output ceiling, Qwen lists web search and function calling. Latency is not publicly documented on this page.

For self-hosting. Kimi K3 is open weight (Kimi K3 License, revenue-triggered obligations above $20M / 100M MAU); Qwen3.8-Max is proprietary and API-only. Hardware requirements and quantization are Not publicly documented on this page.

For coding agents. Both list coding, tool calling and structured output, with shared Terminal-Bench 2.1 at 88.3 vs 86.6 (vendor-reported, different harnesses). K3’s 1M output ceiling is more relevant for long-output agent loops; Qwen’s web search is a distinct tool surface.

For enterprise. Deployment differs: Kimi offers self-hosting under a conditional license; Qwen offers a six-region API footprint. Region, SLA, data-residency and compliance terms are not publicly documented — confirm with the vendor.

What is uncertain

• Even shared benchmark names come from different harnesses, so scores are not strictly comparable.
• Kimi K3’s input-modality boundary is inconsistent across Moonshot’s official sources.
• No third-party benchmark evidence is recorded for either model (Kimi K3 cites some Artificial Analysis comparisons, but its own rows are vendor-reported).

Sources

• Moonshot AI — Kimi K3 (GitHub)
• Kimi platform — Models documentation
• Alibaba Cloud Model Studio — Qwen3.8-Max
• Alibaba Cloud Model Studio — Model pricing
• Hugging Face model card — Qwen3.8-2.4T-A95B

Labels used above: Official fact (prices, context windows, capabilities and license terms from primary provider sources), Vendor-reported claim (benchmark scores), Third-party evidence (Artificial Analysis, where Kimi K3 cites it), and China AI Hub analysis (the “why each difference matters” reasoning, introduced as analysis).