GLM-5.3-Flash: Features, Benchmarks, Pricing, and How It Compares

Z.ai has released GLM-5.3-Flash, the cost-optimized sibling to its flagship GLM-5.3, and it arrived under an anonymous preview codename before being revealed publicly as "Ox Alpha." Qwen3.8-Flash-Next launched on the same day, so the budget-tier LLM segment got busy quickly.

If you have been watching the race between open-weight Chinese labs and the Western frontier, this one is worth a look.

The main claim is simple: near-frontier coding and agentic performance at a fraction of the cost.

GLM-5.3-Flash scores 84.3 on Terminal-Bench 2.1, which puts it within striking distance of Claude Opus 4.8 (85.0) and GPT-5.6 Terra (87.4).

It uses a mixture-of-experts design in a 320B-A18B configuration (320B aggregate parameters, 18B active per token), supports a 1M token context window, and is natively multimodal.

In this article, I'll cover everything new with GLM-5.3-Flash, looking at the features and exploring the benchmarks.

If you want to compare it against the other budget-tier model that shipped the same day, see our guide to Qwen3.8-Flash-Next, and for a similar MoE model we ran locally, check out our tutorial on running DeepSeek-V4-Flash-0731.

In a Nutshell

• GLM-5.3-Flash is Z.ai's cost-optimized model, codenamed "Ox Alpha," with a 320B-A18B MoE design and 1M token context.
• It scores 84.3 on Terminal-Bench 2.1, near Claude Opus 4.8 (85.0) and behind GPT-5.6 Terra (87.4).
• Blended price is roughly $0.10 per 1M tokens, about a tenth of GLM-5.3's ~$0.90.
• Trade-offs: ~49 tokens/sec output speed and a ~306 GiB FP8 checkpoint that rules out lightweight local hosting.
• Qwen3.8-Flash-Next launched the same day, so the budget tier is worth shopping around in.

What Is GLM-5.3-Flash?

GLM-5.3-Flash is Z.ai's cost-optimized model in the GLM-5.3 family, sitting below the full-fat GLM-5.3 flagship on capability but far below it on price.

Artificial Analysis rates it at an intelligence index of 57, compared to 60 for GLM-5.3.

The trade is speed and cost: Flash serves at roughly one-tenth of the price of GLM-5.2, or about one-ninth of GLM-5.3's blended price and runs slower per token.

The model shipped first as an anonymous preview before being named, and the community quickly attached the "Ox Alpha" codename to it.

The public checkpoint is available as zai-org/GLM-5.3-Flash on Hugging Face, with long-context support (~1M tokens) and tool-calling and multimodal capability exposed through the surrounding ecosystem.

The benchmark that matters most here is Terminal-Bench 2.1, where GLM-5.3-Flash hits 84.3. That is above the 82.7 we saw for DeepSeek-V4-Flash-0731, and only 0.7 points behind Claude Opus 4.8. For a model with a blended usage-weighted price estimated around $0.10 per 1M tokens, that is the story of the release.

GLM-5.3-Flash Key Features

GLM-5.3-Flash is built for developers who want agentic and coding performance without flagship pricing.

Here are the capabilities that matter for practical work.

Near-frontier coding at budget cost

You can run coding agents and automation workflows at a price point that makes high-volume use realistic.

GLM-5.3-Flash scores 84.3 on Terminal-Bench 2.1 and 63.4 on DeepSWE v1.1, up sharply from GLM-5.2's 46.2 on the same DeepSWE eval.

For practitioners, the appeal is running coding tasks that would be expensive on Opus-class models.

Some comparisons frame Flash as "strong" for coding and automation agents at substantially lower cost than GLM-5.3, and the DeepSWE jump over GLM-5.2 suggests the coding improvements are real rather than benchmark-specific.

1M token context window

You can feed entire codebases, long documents, or multi-file agent traces into a single request.

GLM-5.3-Flash supports a 1M token context window, matching GLM-5.3 and the 1M windows on GPT-5.6 Terra and Claude Sonnet 5.

However, some community reports mention attention "drift" at extreme lengths beyond roughly 700K tokens, a limitation shared by most peers with large context windows.

Good recall across hundreds of thousands of tokens is reported, but do not assume perfect retrieval at the far end of the window.

Tool calling and structured output

You can wire GLM-5.3-Flash into agent frameworks with function calls and enforced JSON schemas. The model scores 78.4 on Toolathlon and 48.8 on AutomationBench, the latter a large jump from GLM-5.2's 26.2.

One thing to watch: support varies by provider.

Some gateways mark structured output as unsupported, and system prompt handling differs between hosted providers, so you may need to adapt your tool schema and JSON mode per endpoint.

Native multimodal input

You can pass images alongside text for chart reading, document analysis, and visual reasoning.

GLM-5.3-Flash scores 89.4% on CharXiv-R, 80.5 on MMVU, and 78.0 on Chartography, which point to solid chart and visual-document handling.

The multimodal support is native rather than bolted on, though, as with tool calling, the exact surface depends on which provider you use. A bare Hugging Face checkout may not expose everything the hosted API does.

GLM-5.3-Flash Benchmarks

GLM-5.3-Flash scores 57 on the Artificial Analysis Intelligence Index v4.1.1 at $0.045 per task (discounted). Z.ai says that level of intelligence previously cost roughly ten times as much.

The six-benchmark evaluation below compares Flash against GLM-5.2, DeepSeek-V4-Vision-Exp, Claude Opus 4.8, GPT-5.6 Terra, and Gemini 3.7 Flash.

DeepSWE v1.1

On this software-engineering agent benchmark, GLM-5.3-Flash scores 63.4, ahead of Claude Opus 4.8 (58.0) and DeepSeek-V4-Vision-Exp (59.3), and up from GLM-5.2's 46.2.

It trails GPT-5.6 Terra (69.6) and Gemini 3.7 Flash (65.3), but beating Claude at Flash cost is the practical headline here.

AutomationBench v1.0.6

GLM-5.3-Flash scores 48.8 on multi-step automation workflows, the largest lead over Claude Opus 4.8 (41.0) and DeepSeek-V4-Vision-Exp (38.8) of any benchmark in the set.

GLM-5.2 scored 26.2, so this is also the sharpest generation-on-generation improvement.

GDPVal-AA v2

On this general development and programming evaluation, Flash tops the leaderboard at 1773, ahead of DeepSeek-V4-Vision-Exp (1675), Claude Opus 4.8 (1582), GPT-5.6 Terra (1571), and Gemini 3.7 Flash (1527).

Terminal Bench 2.1

Flash scores 84.3 on terminal and shell agent tasks, sitting just below Claude Opus 4.8 (85.0) and Gemini 3.7 Flash (85.8), and behind GPT-5.6 Terra (87.4). It edges out GLM-5.2 (81.0) and DeepSeek-V4-Vision-Exp (83.9). Competitive, not dominant.

GLM-5.3-Flash Pricing and Availability

GLM-5.3-Flash is available through Z.ai's API and via multiple third-party gateways, including Baseten, Deep Infra, Venice AI, and Requesty.

The public checkpoint zai-org/GLM-5.3-Flash is on Hugging Face for self-hosting.

Z.ai's official base list pricing and its limited-time promotion are as follows:

• Base input: $0.15 / 1M tokens (promo: $0.075)
• Base output: $0.03 / 1M tokens (promo: $0.015)
• Cached input: $0.50 / 1M tokens (promo: $0.25)

Apidog reports a blended, usage-weighted price of roughly $0.10 per 1M tokens for Flash, versus about $0.90 for GLM-5.3.

Third-party gateways price it differently: EmpirioLabs at $0.07 input / $0.25 output, Venice AI at $0.09 / $0.31, and Requesty at $0.14 / $0.45 with a 55%-off promo noted until 9 Sep 2026.

The catch is throughput and hardware.

Flash serves at ~49 tokens/sec versus ~86 for GLM-5.3, and Artificial Analysis rates it 2/4 on speed.

Self-hosting is not lightweight either: the FP8 checkpoint is ~306 GiB in weights alone before KV-cache and runtime, so you need multiple high-memory GPUs (think >4×80 GiB class) rather than a single card.

GLM-5.3-Flash vs GLM-5.3: Which to Use

The choice between the two comes down to cost versus intelligence index and throughput. The table below summarizes the trade.

Pick Flash when cost-per-token is the constraint and your work is coding or agentic. Pick GLM-5.3 when you need the extra intelligence index and roughly double the throughput.

GLM-5.3-Flash vs Qwen3.8-Flash-Next

Both models launched on August 26, 2026, targeting the same budget-agentic tier. GLM-5.3-Flash edges Qwen3.8-Flash-Next on the one benchmark they share directly (DeepSWE v1.1: 63.4 vs 58.7), but the two models are built differently enough that the choice depends more on your workload than on any single score.

GLM-5.3-Flash activates three times as many parameters per token, which likely explains the DeepSWE lead and Terminal-Bench 2.1 result (84.3).

Qwen3.8-Flash-Next counters with stronger reported scores on SWE-bench Pro (62.5) and office and agent benchmarks like CoWorkBench (73.9) and JobBench (55.7), where GLM does not publish comparable figures.

On price, GLM is cheaper during its promotional window. On parameter efficiency, Qwen does more with a smaller active footprint. On self-hosting, Qwen's smaller checkpoint is significantly easier to run locally.

If your priority is coding agents and raw agentic throughput, GLM-5.3-Flash has the stronger numbers where comparison is direct. If your workflows are office-heavy, multimodal, or benefit from a smaller self-hosted checkpoint, Qwen3.8-Flash-Next is worth testing first.

See our full guide to Qwen3.8-Flash-Next for a complete breakdown.

Final Thoughts

GLM-5.3-Flash is Z.ai making a clear bet on the budget-agentic segment: get within a point or two of Opus 4.8 and GPT-5.6 Terra on coding evals, then undercut them by an order of magnitude on price.

The 84.3 on Terminal-Bench 2.1 at roughly $0.10 blended per 1M tokens is the pitch, and it is a strong one for high-volume coding and automation.

The loimitations come with speed and infrastructure.

At ~49 tokens/sec it is not the model for latency-sensitive streaming, and the ~306 GiB FP8 checkpoint rules out casual local deployment. Early preview users on r/opencodeCLI reported queue times and slow starts under load, though GA has likely improved that.

My take is that if cost-per-token is your binding constraint and your work is coding or agentic, GLM-5.3-Flash is worth testing against GPT-5.6 Terra and Claude Opus 4.8 before you commit.

If you need the extra 3 points of intelligence index and faster throughput, the full GLM-5.3 exists for a reason. It also lands the same day as Qwen3.8-Flash-Next, so the budget tier is worth shopping around in right now.

If you want to get comfortable working with models like this, I recommend our AI Fundamentals skill track to build the groundwork, then our tutorial on running a MoE Flash model locally for the hands-on side.