GLM 5.3 Flash vs DeepSeek V4 Flash: Which Cheap Open Model Wins?

Quick answer: GLM 5.3 Flash is the smarter and cheaper-to-blend model; DeepSeek V4 Flash is more than twice as fast. GLM scores 57 on Artificial Analysis's Intelligence Index against DeepSeek's 52, and blends to $0.10 per 1M tokens against DeepSeek's $0.23 — but DeepSeek pushes 119.4 output tokens/second against GLM's 50.2. If your bottleneck is quality or cost, take GLM. If it is wall-clock throughput, take DeepSeek. If you need native image and video input, only GLM qualifies.

These two models look almost interchangeable on a spec sheet: both are MIT-licensed open-weight Mixture-of-Experts models, both advertise a 1M-token context window, both are the cheap tier of a Chinese lab's flagship family, and both are priced within pennies of each other on list input. That surface similarity is exactly why the choice goes wrong so often — the differences that matter are not on the front of the spec sheet.

Everything below is sourced from Artificial Analysis's independent measurements (which run both models through the same harness, so the numbers are actually comparable), the two vendors' official pricing, and the Hugging Face model cards. Verified August 27, 2026. We have not run a head-to-head evaluation of our own, and we flag every place where the public record is thin rather than filling the gap with a guess.

What This Article Solves

The pain point: the two models' headline numbers point in opposite directions, and most comparisons only quote one axis. GLM wins on intelligence and blended price. DeepSeek wins decisively on speed and on raw output-token cost. An article that quotes only the price table concludes DeepSeek is cheaper; one that quotes only the intelligence index concludes GLM is better. Both are technically true and neither is a decision.

You will leave with a side-by-side on all five axes that actually differ, the specific arithmetic that flips the cost answer, and a four-line decision rule.

Side by Side

Round 1: Cost — and Why the Answer Flips

This is the axis where naive comparison fails hardest.

On list output tokens, DeepSeek is much cheaper. $0.28 versus $0.50 per 1M — a 44% saving on the most expensive half of a bill. For output-heavy workloads (long-form generation, verbose agents, code synthesis), DeepSeek wins on raw rate and it is not close.

On blended cost, GLM is much cheaper. Artificial Analysis blends at $0.10 for GLM against $0.23 for DeepSeek — GLM is 2.3x cheaper. The reason is GLM's cached-input rate of $0.03 per 1M, five times cheaper than its fresh input rate. In a 7:2:1 cache:input:output mix, most of your tokens bill at that $0.03 rate.

So which is it? The arithmetic that decides:

• Cache hit rate above roughly 50%, input-heavy traffic → GLM 5.3 Flash. Repository-scale coding agents, document pipelines with a fixed system prompt, RAG with a stable corpus prefix. The $0.03 cached rate dominates everything else.
• Low cache reuse, output-heavy traffic → DeepSeek V4 Flash. One-shot generation, varied prompts, chat where every conversation is different. GLM's cache advantage never activates and you are left paying $0.50 output against $0.28.

The full provider-by-provider breakdown is in our GLM 5.3 Flash pricing guide. There is also a temporary factor: GLM 5.3 Flash is currently running a 50% launch discount on some providers ($0.075 / $0.25 on Z.AI, Novita, GMICloud). That makes it unambiguously cheaper today on every axis. It is a promotion, so do not build a 12-month forecast on it.

Round 2: Intelligence

Artificial Analysis puts GLM 5.3 Flash at 57 and DeepSeek V4 Flash at 52 on its Intelligence Index. Same lab, same harness, so this is a genuinely comparable five-point gap in GLM's favour, and it is consistent with GLM being the newer model with more active parameters (18B vs 13B).

Z.ai's own launch benchmarks reinforce the direction — 84.3 on Terminal-Bench 2.1, 63.4 on DeepSWE v1.1 — but those are vendor-run and did not use DeepSeek V4 Flash as the comparison, so they establish that GLM is near-frontier without settling the head-to-head. Take the Artificial Analysis number as the credible one.

Five points on a 100-ish-point index is real but not transformative. It is the difference between "handles the moderately hard task" and "handles the moderately hard task more reliably" — not a different capability class.

Round 3: Speed — DeepSeek's Decisive Win

119.4 tokens per second versus 50.2. DeepSeek V4 Flash produces output 2.4x faster than GLM 5.3 Flash, and it also reaches first token faster (1.12s vs 1.47s).

This is the single largest gap between the two models, and it is the one most likely to be missed because both models have "Flash" in the name. DeepSeek's Flash is a speed tier. GLM's Flash is a price tier. Anyone who assumes naming conventions transfer between vendors will make the wrong pick here.

The practical consequence scales with your job shape. For an interactive chat UI, 50 t/s is acceptable — a human reads at roughly that rate. For a batch pipeline generating 10,000 long documents, a 2.4x throughput difference is a 2.4x difference in wall-clock time and in how much parallel capacity you must provision. That provisioning cost does not appear in any per-token price table.

Round 4: Multimodality — GLM's Decisive Win

GLM 5.3 Flash is natively multimodal: text, image, and video in, text out, with visual data in the 30T-token pre-training corpus rather than added via an adapter afterwards. It is the first natively multimodal model in the GLM-5 family, and Z.ai reports 62.4 on OfficeQA Pro, a document-and-visual reasoning benchmark, claiming a lead over Claude Opus 4.8 there.

DeepSeek V4 Flash's mainline release is text. Vision capability lives in a separate experimental branch rather than in the model you would call for general work.

If your workload includes screenshots, UI mockups, PDFs with figures, diagrams, or video frames, this is not a tiebreaker — it is a disqualifier for DeepSeek. One model does the job and the other does not.

The fastest way to find out whether native multimodality actually helps your task is to feed it a real artefact. Try GLM 5.3 Flash on glm5.app, drop in a screenshot of something genuinely broken in your product, and see whether the diagnosis is usable.

Round 5: Self-Hosting

Both are MIT-licensed, which means both are genuinely free to deploy, fork, and commercialise with no revenue threshold and no acceptable-use appendix. That is where the parity ends.

Weight size is the differentiator, and it is a large one. GLM 5.3 Flash's FP8 release totals roughly 328 GB; DeepSeek V4 Flash is around 160 GB — per file-size readings reported by users on NVIDIA's developer forums, which is a community source rather than a vendor spec. Roughly double the memory footprint means roughly double the hardware.

Practically: on the DGX Spark class of hardware, forum discussion puts DeepSeek V4 Flash within reach of a small setup while GLM 5.3 Flash needs multiple clustered units with 4-bit or hybrid quantization to fit at all. GLM's officially supported serving stacks are SGLang, vLLM, TokenSpeed, and KTransformers; community GGUF quantizations exist for llama.cpp, Ollama, LM Studio, and Jan.

If self-hosting on modest hardware is the plan, DeepSeek V4 Flash is substantially easier to live with. That is an infrastructure fact, not a quality one, and it will decide more real deployments than the five-point intelligence gap will.

The Decision Rule

Four lines. Find yours and stop reading.

1. You need image or video input → GLM 5.3 Flash. DeepSeek's mainline does not do it.
2. Throughput is your bottleneck (batch pipelines, high-concurrency serving, latency SLOs) → DeepSeek V4 Flash, 2.4x faster output.
3. Cache-heavy, input-heavy traffic (coding agents, long-context documents, stable system prompts) → GLM 5.3 Flash. The $0.03 cached rate is the cheapest thing in this comparison.
4. Self-hosting on limited hardware → DeepSeek V4 Flash, at roughly half the weight footprint.

If none of those describes you — mixed traffic, hosted API, no strong constraint — GLM 5.3 Flash is the better default on the strength of the intelligence gap and the blended price, with the caveat that the discount currently flattering its price is temporary. The cheapest way to break the tie is empirical: run the same ten hard prompts through both. Open a GLM 5.3 Flash session for one half of that test and use the public API ID glm-5.3-flash if you want to script it.

Frequently Asked Questions

Which is cheaper, GLM 5.3 Flash or DeepSeek V4 Flash? It depends on your cache hit rate. On raw output tokens DeepSeek is cheaper ($0.28 vs $0.50 per 1M). On Artificial Analysis's blended 7:2:1 metric GLM is 2.3x cheaper ($0.10 vs $0.23), because GLM's cached-input rate is $0.03 per 1M. High cache reuse favours GLM; low reuse with heavy output favours DeepSeek.

Which is faster? DeepSeek V4 Flash, clearly — 119.4 output tokens/second versus 50.2, plus a faster time to first token (1.12s vs 1.47s). GLM's "Flash" refers to price, not latency.

Which is smarter? GLM 5.3 Flash, by five points on Artificial Analysis's Intelligence Index (57 vs 52). Same lab, same harness, so the comparison is fair — though five points is an incremental edge rather than a capability jump.

Can both handle 1M tokens? Both advertise a 1,048,576-token context window. Neither has published long-context degradation curves, so treat the ceiling as a specification rather than a guarantee and test retrieval accuracy at your actual working length.

Are both open source? Both release weights under the MIT license — commercial use, modification, and redistribution permitted. GLM's weights are at zai-org/GLM-5.3-Flash on Hugging Face. Note that GLM's code repository (zai-org/GLM-5) is Apache-2.0 while the weights are MIT.

Sources

• Artificial Analysis — GLM-5.3-Flash — Intelligence Index, output speed, TTFT, blended price for GLM.
• Artificial Analysis — DeepSeek V4 Flash — the same metrics for DeepSeek, measured on the same harness.
• zai-org/GLM-5.3-Flash — Hugging Face model card — parameters, MIT license, multimodality, benchmark table.
• DeepSeek API pricing — first-party DeepSeek V4 Flash rates.
• GLM 5.3 Flash on OpenRouter — GLM pricing, context, provider list, and the launch discount.

Verified August 27, 2026. GLM 5.3 Flash's discounted rate is promotional and will change; self-hosting weight sizes are community-reported from NVIDIA developer forum readings, not vendor specifications.