GLM-5.3-FlashX vs DeepSeek V4.1 Flash: Which One Is Right for You?

The two models side by side

	GLM-5.3-FlashX	GLM-5.3-Flash	DeepSeek V4.1 Flash
Model ID	glm-5.3-flashx	glm-5.3-flash	deepseek-flash
Announced	September 18, 2026	August 2026	September 10, 2026
Parameters	320B total / 18B active	320B total / 18B active	552B backbone, 8B active in prefill / 16B in decode
Context window	1M tokens	1M tokens	1M tokens
Maximum output	128K tokens	128K tokens	384K tokens
Input modalities	Video, image, file, text	Video, image, file, text	Image and text
Thinking control	enabled only	enabled only	On by default, can be disabled; reasoning effort 1–100
Open weights	Yes (base model, August 26)	Yes	Yes, MIT license, FP8
Published speed	Up to 200 tokens/s	Not published	Not published

Both are Mixture-of-Experts models with aggressive long-context optimization, and both are explicitly built to make 1M-token contexts affordable: GLM-5.3-Flash with a hybrid sparse-and-linear attention stack, DeepSeek V4.1 Flash with compressed sparse attention and FP4 KV caching.

Prices: where they are similar, and where they are not

Official list prices per 1M tokens, checked September 18, 2026:

Token type	GLM-5.3-FlashX	GLM-5.3-Flash	DeepSeek V4.1 Flash (off-peak)	DeepSeek V4.1 Flash (peak)
Input, cache miss	$0.37	$0.15	$0.15	$0.30
Input, cache hit	$0.075	$0.03	$0.003	$0.006
Output	$1.25	$0.50	$0.60	$1.20

Three things stand out.

1. The cache-hit price is the real gap. DeepSeek's cached input is 10× cheaper than GLM-5.3-Flash's and 25× cheaper than GLM-5.3-FlashX's. Cache-hit pricing only applies to tokens the provider actually records as cached, so the size of the win depends on how repetitive your traffic is — but for agent workloads that resend a large prefix on every turn, it is the single biggest cost lever in this comparison.

2. Uncached input and output are close. DeepSeek's off-peak input price equals GLM-5.3-Flash exactly, and its output price sits between GLM-5.3-Flash and GLM-5.3-FlashX. At peak, DeepSeek's output price ($1.20) is almost identical to GLM-5.3-FlashX's ($1.25).

3. The discount mechanics are different. DeepSeek discounts the API price itself: off-peak is half of peak, and peak hours are Monday–Friday 01:00–04:00 and 06:00–10:00 UTC, so most of the week is off-peak. Zhipu's comparable discount is on the GLM Coding Plan, where off-peak calls consume 50% of the standard points — a subscription benefit, not a lower API rate. GLM API pricing is flat, and cached-input storage is listed as limited-time free.

What a real workload costs

Same token volumes, list prices, no route multipliers:

Workload	GLM-5.3-FlashX	GLM-5.3-Flash	DeepSeek V4.1 Flash
10M uncached input + 1M output	$4.95	$2.00	$2.10 off-peak / $4.20 peak
20M cached input + 0.5M output	$2.13	$0.85	$0.36 off-peak / $0.72 peak
2M uncached input + 4M output	$5.74	$2.30	$2.70 off-peak / $5.40 peak

Read the three rows as three product shapes:

• Output-heavy generation (large diffs, whole-file writes, long reports) is where GLM-5.3-Flash is cheapest and DeepSeek is close behind off-peak.
• Cache-heavy agent loops are where DeepSeek pulls away, by a factor of 2.4 against GLM-5.3-Flash and nearly 6 against FlashX.
• FlashX buys latency, not price. It carries a 2.5× premium on input and output over GLM-5.3-Flash, so it makes sense when a slow turn costs you more than the tokens do.

Capability differences that change the decision

Output length. DeepSeek allows up to 384K output tokens per response — three times GLM's 128K ceiling. For whole-repository refactors or long single-pass document generation, that ceiling can be the deciding constraint.

Thinking control. DeepSeek V4.1 Flash runs thinking by default but lets you disable it, and exposes a continuously adjustable reasoning effort from 1 to 100. GLM-5.3-Flash/FlashX only supports thinking.type: enabled; thinking cannot be switched off, so every request pays reasoning tokens. If you need low-latency, low-cost simple calls, DeepSeek gives you a dial that GLM does not.

Multimodal reach. Both accept images, but GLM-5.3-Flash is documented with video and file input as well. If your pipeline feeds screen recordings, PDFs, or mixed media into the model, GLM covers more ground out of the box.

Open weights and licensing. GLM-5.3-Flash's base model was open-sourced on August 26, 2026 (320B-A18B). DeepSeek V4.1 Flash publishes FP8 weights under an MIT license with a technical report. Both are self-hostable in principle; check the license and hardware requirements against your own deployment before assuming equivalence.

Throughput ceilings. DeepSeek publishes a concurrency limit of 2,500 for Flash (against 500 for V4 Pro). Zhipu does not publish an equivalent number, so verify throughput with your provider rather than assuming parity.

Speed: they are in the same class now

Zhipu publishes up to 200 tokens/s for GLM-5.3-FlashX, delivered on a serving stack built for the Flash architecture — a dedicated SGLang-based inference engine, memory optimizations for 1M-token contexts, and a 3× end-to-end serving improvement over its initial baseline on the same hardware.

DeepSeek does not publish a tokens-per-second figure for V4.1 Flash. Its documentation claims better speed and lower total completion time than V4 Pro; throughput reported by developers lands in the same ~200 tokens/s range.

That is the honest framing: these two are no longer separated by raw speed. Vendor-published peaks and observed numbers are measured differently, on different hardware, with different prompt shapes. Measure time to first token, sustained output rate, and total task time on your own prompts before choosing on speed. The earlier complaint that GLM's Flash tier felt slow is the specific problem FlashX was built to fix, and that is worth retesting — not treating as settled by a spec sheet.

How to read the benchmark numbers

Zhipu reports GLM-5.3-Flash at 63.4 on DeepSWE v1.1 (against 46.2 for GLM-5.2), 48.8 on AutomationBench (against 26.2), and 29.0 on Z.ai Code Bench v1.0 at maximum effort against Claude Opus 4.8's 29.5 in the same table. On the DeepSeek side, the V4.1 Flash base model reports MMLU-Pro 74.1 and BigCodeBench 60.6 within its own published evaluation set.

These are vendor numbers from different harnesses, different evaluation sets, and different dates. Do not compare them across the two columns. What they establish is that both labs place their Flash tier close to their own frontier models on agentic and coding tasks. Whether that holds for your repository is a question only your own evaluation set can answer.

Coding: which one?

The short version:

• GLM-5.3-FlashX exists to fix the "too slow" complaint that dogged earlier GLM Flash usage. If you tried GLM for coding, liked the quality, and moved on because of latency, this is the version worth retrying — same model ID family, faster serving tier.
• Keep DeepSeek V4.1 Flash where cache economics dominate — long agent loops that resend a large context every turn, or high-volume batch coding work where cost per finished task matters more than interactive feel.
• Skip the benchmark comparison. The two labs measure different things with different harnesses. Run twenty real tasks from your own repository through both, and compare completion rate, rework, total cost and wall-clock time.

How to call both models

Both use OpenAI-compatible chat completions, so a single client can target either. The model IDs are glm-5.3-flashx and deepseek-flash.

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_APIMASTER_KEY",
    base_url="https://apimaster.ai/v1",
)

response = client.chat.completions.create(
    model="glm-5.3-flashx",
    messages=[
        {"role": "user", "content": "Review this diff for correctness and propose tests."}
    ],
    temperature=1,
    top_p=0.95,
    extra_body={
        "reasoning_effort": "max",
        "thinking": {"type": "enabled", "clear_thinking": False},
    },
)

print(response.choices[0].message.content)

Swap model to deepseek-flash for the DeepSeek route. Parameter expectations differ in three ways worth knowing before you write shared code:

Setting	GLM-5.3-FlashX	DeepSeek V4.1 Flash
Thinking	enabled only, cannot be disabled	On by default; can be disabled
Reasoning effort	reasoning_effort: max recommended	Adjustable on a 1–100 scale
Streaming	stream: true plus tool_stream: true	Standard streaming; FIM available in non-thinking mode

Both models support tool calling, structured/JSON output, and context caching. DeepSeek additionally documents the Anthropic-format API and the Responses API, which can simplify reuse of harnesses written for those formats.

Run both through one API key on APIMaster.ai

APIMaster exposes the GLM and DeepSeek families behind one OpenAI-compatible endpoint, so you can evaluate both tiers with the same key, the same console, and the same billing — pay-as-you-go from $1, with up to 70% off official rates on selected routes.

1. Create an APIMaster account.
2. Add pay-as-you-go credit, starting from $1.
3. Create an API key in the APIMaster console.
4. Point your client at https://apimaster.ai/v1 and switch the model field to compare.

Register for APIMaster · Explore the model marketplace · Test model identity

FAQ

Which is cheaper, GLM-5.3-FlashX or DeepSeek V4.1 Flash? It depends on the workload. DeepSeek is far cheaper for cache-heavy traffic ($0.003 vs $0.075 per 1M cached input tokens) and cheaper on output at peak. GLM-5.3-Flash (not FlashX) is the closer price match, and is the cheapest of the three for output-heavy generation.

Why is DeepSeek's cache-hit price so much lower? DeepSeek designed V4.1 Flash around KV-cache compression and charges $0.003 per 1M cached input tokens off-peak. Cached pricing applies only to tokens the provider records as cache hits, so the actual saving depends on how repetitive your prompts are.

Do both support a 1M-token context window? Yes. Both list 1M tokens. The maximum output per response differs: 384K tokens for DeepSeek V4.1 Flash, 128K for GLM-5.3-Flash and FlashX.

Can I turn thinking off? On DeepSeek V4.1 Flash, yes — thinking is on by default but can be disabled, and reasoning effort is adjustable from 1 to 100. On GLM-5.3-Flash and FlashX, thinking cannot be disabled.

Which one is faster? They are in the same class. Zhipu publishes a 200 tokens/s peak for FlashX; DeepSeek does not publish a figure, and observed throughput is around the same level. Speed depends on the route, prompt shape and concurrency — measure it yourself.

Are both open-weight models? Yes. GLM-5.3-Flash's base model was open-sourced on August 26, 2026; DeepSeek V4.1 Flash publishes FP8 weights under an MIT license with a technical report.

Can I use one API key for both? Yes, on a gateway that serves both families. APIMaster provides one OpenAI-compatible endpoint and key, so you can switch between glm-5.3-flashx and deepseek-flash by changing the model field.

Sources and further reading

• Z.ai — GLM-5.3-Flash/FlashX documentation — specs, capabilities, recommended settings and serving details
• Z.ai — Pricing — official GLM list prices per 1M tokens
• DeepSeek — Models & Pricing — official model details, pricing, peak-hour schedule and concurrency limits
• DeepSeek — Vision guide — image input formats and request examples
• Hugging Face — deepseek-ai/DeepSeek-V4.1-Flash — MIT-licensed weights, architecture notes and evaluation tables
• Hugging Face — zai-org/GLM-5.3-Flash — open weights for the GLM Flash base model

All sources checked on September 18, 2026. Prices change; verify current terms before committing a large workload.