SyncDev model directory · Profile updated Oct 13, 2026
GLM 5.3 Flash is an MIT-licensed multimodal MoE checkpoint for text, visual understanding, coding and agent workflows.
The specification score summarizes recorded features and limits. It is not a benchmark result.
Recorded modality support shows what this model can accept and produce. An undocumented modality is shown as unknown rather than assumed to be unsupported.
Accepts
Produces
GLM-5.3-Flash supports text, images, video and files, with 320B total parameters, 18B active, a 1M context and a 128K output limit. Reasoning cannot be disabled. The FlashX API ID is a serving option in the same family and is not currently included in the GLM Coding Plan. Check the selected route’s access and price; do not transfer FlashX throughput claims to a local Flash deployment.
Keep the hosted model ID separate from a downloadable checkpoint name. A local serving engine can expose a different subset of tool, visual and structured-output features from the first-party service. Replay the application’s message history and tool results with the exact interface and reasoning settings you intend to use.
A documented context or output allowance describes capacity, rather than accuracy on a particular task. Include prompts, retained reasoning, tool definitions and tool results when budgeting a long interaction. Use the selected route’s current billing unit and account terms; an absent price is not a claim of free usage. Check migration behavior before replacing a pinned model or relying on a moving alias.
Build a small evaluation set from the work your application actually handles. Include incomplete information, contradictory inputs and a case that should stop for clarification. Define an acceptable deliverable before running the model, then inspect failures as carefully as successful examples. Keep input material, tools and effort settings consistent when comparing candidates.
Record the exact model identifier, deployment route and request settings with each result. Count complete runs, including rejected outputs, retries and tool calls. When the workflow creates files or code, open the files or execute the existing checks before accepting the result. A plausible first response can hide an incomplete final deliverable.
The specifications describe supported interfaces and request capacity. They do not establish a measured quality ranking or a latency guarantee for your account. SyncDev has not independently benchmarked this model. Choose a deployment after testing the languages, input formats and operating conditions that matter to your users.
Explore zhipuai models, browse model tasks, or compare two models with the same acceptance criteria.
Compare the model on the work you need to complete.
GLM 5.3 Flash is listed for Image-text-to-text, Text generation, Video-text-to-text, Coding & Code Generation. It accepts text, image, video, file and produces text. Use the publisher’s input format and preprocessing for your chosen deployment.
Documented limits: 1M context and 128K maximum output. Measure a completed task using the prompts, documents and tools your application will use. Include reasoning and retries in the result.
The weights are downloadable under MIT. Check runtime support and size the complete model and working context for your expected concurrency.
These records share a directory category. Check the source card, output type and deployment requirements before treating a model as a replacement.
Gemini 4 Argon handles coding, reasoning and multimodal work with a shared 1M-token window and background long decode.
Meta
Meta’s Muse Wonder handles coding, computer use and sustained agent tasks through Meta Model API and Muse Code, with a 1M-token context.
Meta
Muse Wonder Full is Meta’s higher-capability Wonder variant for sustained coding and agent work, available through eligible Muse Code subscriptions.
Xiaomi
MiMo-V2.6-Distill-Qwen-9B is Xiaomi’s smaller downloadable checkpoint for agent research and reinforcement-learning experiments.
Sakana AI
Fugu Max from Sakana AI: Multi-agent model for routing expert agents across complex analytical tasks.
Sakana AI
Fugu Ultra v2 from Sakana AI: Quality-first multi-agent model for hard research, analysis, and competitions.