GLM 5.3 vs Opus 5: Benchmarks, Pricing, and Which to Choose

GLM 5.3 and Opus 5 answer different questions, so the honest comparison starts by admitting what the data can and cannot tell you. GLM 5.3, released by Z.ai on August 14, 2026, is a coding and security specialist. Claude Opus 5, released by Anthropic on July 24, 2026, is a frontier generalist. If you are weighing GLM 5.3 vs Opus 5 for real work, the useful split is coding-and-cost against reasoning-and-breadth, and how much of each win is independently verified.

Why this comparison is hard, and how to read it

Start with the honest limit: GLM 5.3 and Opus 5 have almost no overlapping third-party benchmarks. Z.ai ran GLM 5.3 on its own harness and chose which rivals to include, and Opus 5 was not one of them. Anthropic ran Opus 5 on its own harness. Neither lab ran the other's model, so for most benchmarks you have one side's number and not the other's, and the two you do have came from different setups.

That means a clean, same-harness head-to-head barely exists for these two. Rather than fake one by pairing mismatched numbers, this comparison does three things. It shows each model's benchmark profile on its own terms, with every figure labeled by who ran it. It leans on the two places where an independent evaluator actually ran both models. And it puts the real weight on price, context, modality, and openness, where the two are directly and verifiably comparable, and where the decision usually gets made anyway.

One reason the labels matter: vendor-reported scores run on the vendor's own harness, effort settings, and prompt scaffolding, so the same model can post different scores across sources. A vendor number and an independent number are not the same kind of evidence. Throughout, figures are marked Z.ai-reported, Anthropic-reported, or independent (Artificial Analysis, ARC Prize, MindStudio).

Overview: GLM 5.3 and Opus 5 differ most on cost and openness

The two models sit at opposite ends of the market. GLM 5.3 is pitched as the strongest open-weight coder Z.ai has measured, built for terminal work, long-horizon engineering, automation, and defensive security. Opus 5 is a proprietary frontier model built for agentic coding, scientific work, and demanding knowledge tasks, priced at half of Anthropic's larger Fable 5.

Their headline specs are close. Both carry a context window of roughly 1 million tokens, and both cap output at 128,000 tokens. Opus 5 accepts images; GLM 5.3 is text-only. The real distance shows up in two places: what each costs to run, and whether you can host it yourself.

Table 1: Core specifications at a glance

Specs per Z.ai and Anthropic launch materials, as of August 2026.

The post-training story is the single most important fact about GLM 5.3, so it is worth stating plainly. Z.ai did not build a new base model. GLM 5.3 runs on the same 743B-parameter mixture-of-experts base as GLM 5.2, and every reported gain comes from scaled post-training rather than a bigger or retrained model. That is unusual, and it explains why the benchmark jumps look so large in one release cycle.

Also read our GLM 5.2 benchmark guide to understand how the base model performed before the post-training gains were applied.

GLM 5.3's profile: strong on coding, automation, and security

Read this section as GLM 5.3's own scorecard, not a head-to-head. Every figure here is from Z.ai's launch table, which benchmarks GLM 5.3 against GLM 5.2, Kimi K3, DeepSeek V4 Pro, GPT-5.6 Sol, and Fable 5. Opus 5 is not in it, so there is no Z.ai-run comparison to Opus 5 on any of these rows. What the table does show is where GLM 5.3's post-training push landed: terminal execution, long-horizon engineering, and automation, the tasks where a model has to hold a goal across tools, tests, and recovery loops.

Table 2: GLM 5.3 benchmark profile (all figures Z.ai-reported)

Source: Z.ai launch table, vendor-reported, August 2026, at max reasoning effort. GLM 5.2 is shown to size the generational jump, since both share a base and harness.

The cleanest read here is the jump over GLM 5.2, because both models share a base and a single harness, so that column is a true like-for-like. Terminal-Bench 3.0 climbs from 4.6% to 28.3%, DeepSWE from 46.2% to 66.9%, and AutomationBench nearly doubles. In plain terms, a higher score on these means the model needs less hand-holding on real engineering work: it finishes more coding tasks correctly, holds a job together across a terminal session, and completes multi-step automations without a person fixing each stage. AutomationBench is the standout, and it points to a model tuned specifically for process automation.

For an Opus 5 reference point on automation, Anthropic reports Opus 5 at 26.0% on Zapier's AutomationBench. GLM 5.3's 48.2% is higher, but the two ran on different harnesses, so treat that as directional context, not a settled result. The only benchmarks where one evaluator actually ran both models are covered in their own section below.

For the full GLM 5.3 benchmark breakdown and the harness caveats behind each row, see our GLM 5.3 benchmarks guide.

Opus 5's profile: strong on reasoning and knowledge work

Read this section as Opus 5's own scorecard. For the full launch rundown, see our Opus 5 launch coverage. Its standout is ARC-AGI-3, a benchmark built to resist memorization, where a model has to work out unfamiliar rule systems through interaction. Opus 5 scores 30.2%, roughly three times the next-best model, and the ARC Prize Foundation credited the lead to genuinely stronger logical reasoning rather than pattern recall. Anthropic reports a perfect 42 out of 42 on IMO 2026 without tools, above the historical gold-medal threshold of 29.

GLM 5.3 has no published figure on most of these, because Z.ai did not run them. That absence is a data gap, not a GLM score of zero. The one row where both models have a number is the Artificial Analysis Intelligence Index, and it is covered in the shared-comparison section below.

Table 3: Opus 5 benchmark profile

Sources labeled per row. Max reasoning effort, as of August 2026. GDPval figures are Elo ratings.

In plain terms, a strong score on these means the model is better at work that does not look like straightforward coding: solving unfamiliar problems, producing knowledge-work output a human would grade as high quality, and driving a computer or browser through a goal on its own. ARC-AGI-3 is the hardest of these to fake, since it rewards reasoning through something genuinely new rather than pattern-matching to training data, which is why it is the result most worth weighting.

Also read our Claude Opus 5 Reviews for a closer look at how the benchmark scores translate into real-world workflows.

Where they were actually measured together

Two evaluations ran both models under one harness, and they are the closest thing to a real head-to-head this comparison has. They do not fully agree, which is itself the honest takeaway: independent evidence on these two is thin, and it points in slightly different directions.

Table 4: The only same-harness comparisons available

Both evaluators ran both models. Artificial Analysis is a composite capability index at max reasoning effort; KingBench 3 is a fixed coding-and-simulation benchmark. As of August 2026.

The Artificial Analysis Intelligence Index, a composite of nine evaluations, puts Opus 5 at 63 against GLM 5.3 at 60. That is a real gap, though a modest one, and it favors Opus 5 on broad capability. KingBench 3, a fixed coding-and-simulation benchmark, runs the other way, with GLM 5.3 at 91.25% ahead of Opus 5 at 77.5%. One index rewards general reasoning breadth, the other rewards coding and simulation specifically, so the split lines up with each model's design. Neither is a consensus on its own, and two data points is not many. Treat them as the most trustworthy signals here precisely because one evaluator ran both, then confirm on your own tasks.

Also read our GLM 5.3 alternatives guide for what else is worth trying when the benchmark split or text-only limitation becomes a deciding factor.

Security: GLM 5.3's standout, and most sensitive, category

GLM 5.3's most surprising strength is defensive security. On CyberGym, a benchmark for practical vulnerability discovery, Z.ai reports GLM 5.3 at 84.5%, the top row in its launch chart. Z.ai says the capability grew faster than expected as training scaled, and that is the stated reason it held the weights back for a safety review before release.

The security picture has a ceiling, though. GLM 5.3 leads on discovery while trailing on exploit development under a time budget, which matches how Z.ai frames the model as built for cyber defense rather than offense. For teams doing code review, CI security scanning, or static auditing, the discovery strength is worth testing against your own workloads.

Opus 5 is not the security leader here. Anthropic's own launch disclosure places Opus 5 behind its Mythos-class model on cybersecurity, the one category where Anthropic did not claim the top Claude spot.

Pricing: GLM 5.3 costs a fraction of Opus 5

Price is where GLM 5.3 makes its strongest case, and the number is now confirmed. GLM 5.3's API opened on August 18, 2026 at $1.40 input and $4.40 output per million tokens, with cached input at $0.26, the same rate as GLM 5.2. That is roughly five times cheaper than Opus 5 on output. One nuance worth flagging: Z.ai's own pricing page still lists GLM 5.2 as its top row, so the confirmed 5.3 rate currently comes from Z.ai's API and gateways like OpenRouter rather than a dedicated 5.3 line on the pricing table. Our GLM 5.3 pricing guide tracks access and cost as they settle.

Opus 5's pricing is also confirmed and unchanged from its predecessor at $5 input and $25 output. So this is a genuine like-for-like comparison: two confirmed per-token rates, with GLM 5.3 sitting far below Opus 5.

Table 5: Pricing per 1M tokens (pricing as of August 2026)

Both rates confirmed by their vendors. GLM 5.3's rate matches GLM 5.2 and is live on the Z.ai API and OpenRouter.

At those rates the cost gap at volume is real and compounds fast. For agents that chain hundreds of tool calls and burn tens of thousands of output tokens per task, a roughly five-times-cheaper output rate changes the production math. Z.ai also reports better token efficiency, finishing coding tasks in fewer output tokens than a comparable Opus run, though that figure comes from a private Z.ai benchmark run against Opus 4.8, not Opus 5, and should be treated as a vendor signal rather than proof.

To know more, read our Claude Opus 5 pricing guide for a full breakdown of what the token rates mean for your workload.

Openness: the open-weight question is still unsettled

GLM 5.3 was not a downloadable model at launch, and that changes what "open" means for it right now. Z.ai released GLM 5.3 through its API and GLM Coding Plan on August 14, 2026, as covered in our GLM 5.3 launch report, and said the weights would follow roughly two weeks later, after a safety evaluation. GLM 5.2 shipped MIT-licensed weights within days of launch, so GLM 5.3 broke that pattern deliberately.

A promised checkpoint is not a downloadable one. Teams that need to self-host for data residency, cost control, or vendor independence could not do so at release, and the license for GLM 5.3 had not been published either. If open weights are the reason you are considering GLM 5.3, confirm they have actually shipped and check the license terms before committing.

Opus 5 makes no such promise and never has. It is proprietary, available through Anthropic's API and major cloud platforms, with the compliance and managed scaling that implies, and no self-hosting.

Verdict: which model should you choose?

Pick from the workload, then confirm your own tasks before you commit. The benchmark winner matters less than the model that finishes your real work with less cleanup, and with two models this different, the decision is rarely close once you name the job.

Choose Claude Opus 5 when:

• The decision turns on novel reasoning, knowledge work, computer use, or image input.
• A confirmed price and mature enterprise access matter more to you than self-hosting.

Choose GLM 5.3 when:

• The work is terminal-heavy coding, long automation loops, or defensive security.
• Cost at volume drives the architecture, and you can tolerate the launch-day uncertainty around the open weights.

Run each candidate against one bounded task with a defined goal, allowed actions, and a clear definition of a correct result. Measure verified completion, recovery after a failed tool call, human review time, and cost per accepted result. Keep model selection behind a stable interface so you can switch providers without rewriting your product. If your shortlist is within the Claude line, our Opus 5 vs Fable 5 breakdown covers that tier choice.

Also read our Opus 5 alternatives guide for what else is worth trying when neither model fits your workload.

Stop comparing models, start building

The GLM 5.3 vs Opus 5 decision comes down to workload: GLM 5.3 for cost-sensitive coding, automation, and defensive security, and Opus 5 for reasoning, knowledge work, and computer use. Verify the numbers that matter to you on your own tasks, especially while GLM 5.3's open-weight release is still settling.

If you are building an actual application rather than benchmarking models, Emergent turns a description into a working, deployable full-stack app, and lets you use Claude, GPT, or Gemini through a single Universal LLM Key with unified billing. You choose the model that fits each project, and Emergent handles the integration, the backend, and the deployment.

Start Building with Emergent.