China AI Hub
Comparisons / DeepSeek-V4.1-Flash vs GLM-5.3-Flash: Budget Tier
DeepSeek-V4.1-Flash vs GLM-5.3-Flash: Budget Tier
Updated: 2026-09-22
Compared entities
Comparison dimensions
Decision rule
Choose DeepSeek if your workload is cache-heavy and can be scheduled off-peak: a 2% cache hit and a queue-friendly discount beat GLM's 20% cache on any agent loop that re-reads context, provided you avoid Beijing peak hours. Choose GLM-5.3-Flash if traffic is interactive and output-heavy: $0.50 output against $0.60, no peak multiplier, and vision in the Flash release. At identical $0.15 input, the decision is entirely about shape of traffic, not price.
All figures below are from the China AI Hub database, last verified 2026-09-22. Where a field is not publicly disclosed, we say so rather than estimating.
At a glance
| Dimension | DeepSeek-V4.1-Flash | GLM-5.3-Flash |
|---|---|---|
| Context window | 1,048,576 tokens | 1,048,576 tokens |
| Maximum output | 393,216 tokens | 131,072 tokens |
| Input price (per 1M) | $0.15 | $0.15 |
| Output price (per 1M) | $0.60 | $0.50 |
| Open weights | Yes (MIT) | Yes (Apache-2.0) |
| API available | Yes | Yes |
| Reasoning | Yes | Yes |
| Coding | Yes | Yes |
| Vision / Video | Yes / Not listed | Yes / Yes |
| Tool calling | Yes | Not listed |
| Function calling | Yes | Not listed |
| Structured output | Yes | Not listed |
| Computer use | Not listed | Yes |
| Agent capability | Not listed | Yes |
| Benchmark records in DB | 5 | 3 |
Pricing
The two lowest-priced flagships in our database sit within cents of each other: both list $0.15 per 1M input tokens; DeepSeek-V4.1-Flash lists $0.60 output and GLM-5.3-Flash lists $0.50 output. (DeepSeek additionally lists off-peak pricing at half the peak rates.) At this tier, per-token differences are small; capability differences matter more.
Context and output
Identical 1,048,576-token context windows. DeepSeek-V4.1-Flash lists a 393,216-token maximum output versus 131,072 for GLM-5.3-Flash — a 3x difference for long generations.
Capabilities
Both list reasoning and coding. The profiles then diverge:
- GLM-5.3-Flash lists video, computer use and agent capability.
- DeepSeek-V4.1-Flash lists tool calling, function calling and structured output, plus vision (no video).
For structured-output pipelines, DeepSeek-V4.1-Flash lists the relevant capabilities; for agent and GUI-automation work, GLM-5.3-Flash lists them.
Openness and deployment
Both are open weight with permissive licenses: MIT for DeepSeek-V4.1-Flash, Apache-2.0 for GLM-5.3-Flash. Both can be self-hosted. China AI Hub analysis: this makes both candidates for local deployment with community quantization.
Benchmarks and verification
The database holds 5 benchmark records for DeepSeek-V4.1-Flash and 3 for GLM-5.3-Flash, labeled by source type and version. We do not rank the models on this page; check each benchmark page for score-by-score source labels.
Benchmark comparability is limited: the two models’ scores come from different test configurations — different benchmark versions, 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.
- Cost: at $0.15 input for both, price is a weak discriminator at this tier; the $0.10 output gap only matters at very high output volume. DeepSeek-V4.1-Flash has an advantage in off-peak scheduling — it lists half-price off-peak rates the other does not.
- Long-context: both list a 1M-token input window, so long-input analysis is equivalent; DeepSeek-V4.1-Flash has an advantage in long single generations because it lists a 3x larger maximum output.
- Reasoning: both list reasoning and neither lists a distinct reasoning tier, so no listed difference on this axis.
- Coding: both list coding; DeepSeek-V4.1-Flash is more relevant when the pipeline needs tool calling, function calling or structured output, which it lists and GLM-5.3-Flash does not.
- Agent / GUI: GLM-5.3-Flash is more relevant when the workload is GUI or agent orchestration — it lists computer use and agent capability.
- Deployment: both are open-weight and self-hostable, so neither has a deployment advantage; the license difference (MIT vs Apache-2.0) is minor, though Apache-2.0’s explicit patent grant can matter in legal review.
- API: both expose an API; the practical choice tracks capability fit (structured/tool surface vs video/computer-use) rather than cost.
Trade-off summary
- Price: essentially tied ($0.15 input both; $0.50 vs $0.60 output).
- Output length: DeepSeek-V4.1-Flash lists 3x the maximum output.
- Tools and structure: DeepSeek-V4.1-Flash lists tool/function calling and structured output.
- Agent/GUI: GLM-5.3-Flash lists computer use, agent capability and video.
- License: both permissive open weight (MIT vs Apache-2.0).
The right pick depends on whether the workload is structured API/agent-tool pipelines or GUI/video automation — the prices are close enough that capability fit should decide. 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. Both list $0.15 input pricing, so cost is a weak discriminator at this tier; the $0.50 vs $0.60 output gap matters only at high output volume. DeepSeek-V4.1-Flash lists tool calling, function calling and structured output, which is more relevant when the pipeline needs structured or tool-driven responses; GLM-5.3-Flash lists none of these. DeepSeek-V4.1-Flash additionally lists half-price off-peak rates for schedulable workloads. Latency is not publicly documented for either model on this page.
For self-hosting. Both are open weight with permissive licenses — MIT (DeepSeek-V4.1-Flash) and Apache-2.0 (GLM-5.3-Flash) — and both can be self-hosted. Hardware requirements, quantization and inference-ecosystem support are Not publicly documented on this page; community quantization is the only deployment detail recorded.
For coding agents. DeepSeek-V4.1-Flash lists tool calling, function calling and structured output, which is more relevant when the agent needs tool-driven or structured responses; GLM-5.3-Flash lists agent capability and computer use but not the tool-calling surface. Both list a 1M-token context. SWE-benchmark and terminal scores are not broken out on this page (only aggregate benchmark-record counts: 5 vs 3).
For enterprise. Region, SLA, data-residency and compliance terms are not publicly documented for either model — confirm with the vendor. Deployment options differ on license: both are self-hostable open weight, with Apache-2.0 carrying an explicit patent grant that may matter in legal review.
What is uncertain
- Benchmark comparability is limited — different versions, harnesses and partial coverage.
- GLM-5.3-Flash lists tool calling, function calling and structured output as “Not listed” — an absence of documentation, not a verified lack of capability.
Sources
- DeepSeek API pricing
- DeepSeek — V4.1-Flash announcement
- Z.ai — GLM-5.3-Flash documentation
- Z.ai — Pricing overview
Labels used above: Official fact (prices, context windows, capabilities and license terms from primary provider sources), Vendor-reported claim (benchmark scores), and China AI Hub analysis (the “why each difference matters” reasoning, introduced as analysis). No third-party benchmark evidence is currently recorded for these models.