GLM 5.3 vs Grok 4.6: Benchmarks, Pricing, and Which to Choose

GLM 5.3 and Grok 4.6 landed two days apart in August 2026, and they rhyme in a useful way. Both are post-training upgrades built on an existing base rather than new foundation models, both undercut the US frontier on price, and both aim at coding agents and long-horizon work. If you are weighing GLM 5.3 vs Grok 4.6, the real questions are how much of each model's claim is independently verified, and which one fits your context and budget.

Why this comparison is hard, and how to read it

Here is the honest problem up front. To compare two AI models fairly, you want to test them the same way: same tasks, same setup, same scoring. That rarely happened with these two. Z.ai tested its own model, GLM 5.3, and chose which rivals to line it up against. xAI tested its own model, Grok 4.6, the same way. Even the few tests both companies happen to report were run separately, under different conditions, so putting those scores side by side shows a rough direction at best, not a fair race.

There is also a bigger difference in how much you can trust each set of numbers. Grok 4.6 has been checked by an outside group, Artificial Analysis, that runs its own tests rather than taking a company's word for it. GLM 5.3 has almost none of that outside checking yet. The reason is simple: an open model can be downloaded and re-tested by anyone, but GLM 5.3's downloadable version was not released at launch, so no outside group could run it. That difference in verification, more than any single score, is the most important thing to take away.

So instead of forcing a head-to-head that the data does not support, this article does three things. It shows what each model scored on its own, and says clearly who did the testing. It highlights the one outside test that actually ran both models under the same conditions. And it puts the most weight on the things you can compare directly and that usually decide the choice anyway: price, how much text the model can handle at once, whether it can read images, and whether you can run it yourself.

Overview: close on price and capability, but split on evidence

The two models are more alike than most frontier pairings. GLM 5.3, released by Z.ai on August 14, 2026, reuses the GLM 5.2 base and takes every gain from scaled post-training. Grok 4.6, released by xAI on August 12, 2026, does the same thing on the Grok 4.5 base. Neither is a bigger model than its predecessor; both spent the upgrade on training rather than scale.

Where they differ is verification, modality, and context. Grok 4.6 accepts images and has independent benchmark coverage. GLM 5.3 is text-only, carries a larger context window, and has no third-party replication of its scores yet.

Table 1: Core specifications at a glance

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

One naming note, because it shows up across sources. xAI merged into SpaceX in early 2026 and now publishes under the SpaceXAI name, so some official pages read "SpaceXAI" while the model and API still use the Grok and xAI labels. We use xAI throughout for clarity.

Shared benchmarks: close, but not a clean comparison

Both vendors report three of the same benchmarks, and GLM 5.3 edges ahead on each. Read that lead with care, though. GLM 5.3's figures are Z.ai-reported and Grok 4.6's are xAI-reported, run on different harnesses and effort settings, so these rows show a rough direction rather than a controlled result. The margins are also small enough that harness variance alone could flip them.

Table 2: Shared coding and agentic benchmarks (mixed sources, see labels)

GLM 5.3 figures vendor-reported by Z.ai. Grok 4.6 figures from xAI's launch table. Different harnesses and effort settings; treat as directional, not head-to-head.

In plain terms, leading on these benchmarks means the model handles real engineering and knowledge work with less supervision: it fixes more bugs correctly across a codebase, stays on a long terminal task without losing the thread, and produces knowledge-work output a human would grade as higher quality. A one-or-two-point gap rarely changes the outcome of a real project, so the honest read is that these two are close rather than one clearly beating the other.

These two models are within a point or two of each other on the work they both measure. Neither pulls away. On DeepSWE and Terminal-Bench, both also trail the top US models: GPT-5.6 Sol leads DeepSWE clearly, and Terminal-Bench 3.0 remains a weak spot for both. Our GLM 5.3 benchmarks guide breaks down the vendor-reported picture in full.

GLM 5.3 does have one category all its own. On CyberGym, a vulnerability-discovery benchmark, Z.ai reports GLM 5.3 at 84.5%, a category-leading score, and that security strength is why Z.ai held the weights back for a safety review. Grok 4.6 does not report a comparable CyberGym result, so there is no fair row to place beside it.

Also read our GLM 5.3 Review for a closer look at how the security strength and other benchmark scores play out in real workflows.

Independent verification: Grok 4.6 is the one that has it

Grok 4.6's biggest advantage over GLM 5.3 is not a benchmark score, it is that an independent lab has actually measured it. Artificial Analysis, a third-party evaluator, scores Grok 4.6 at 61 on its Intelligence Index, tied with GPT-5.6 Sol and one point behind Claude Fable 5. That makes Grok 4.6 one of the cheapest models sitting at the current intelligence frontier by an independent measure.

GLM 5.3 has no Artificial Analysis Intelligence Index score in this battery, so several rows show a Grok figure and no GLM number beside it. The reason is concrete: GLM 5.3's open weights were not released at launch, so no independent lab could download and re-run it the way open-weight models are normally checked. The gap reflects what has been independently measured so far, not a weakness in GLM's underlying scores.

Table 3: Independent benchmarks (Artificial Analysis, high reasoning effort)

Source: Artificial Analysis, captured August 2026, high reasoning effort. GLM 5.3 was not listed on these at the time of writing.

In practice, a strong GPQA Diamond result points to a model that reasons well through hard graduate-level science questions, CursorBench tracks how cleanly it edits code inside a real repository, and the long-context score reflects how reliably it holds detail across a very large prompt. The bigger point is not any single number, though. It is that an outside lab measured Grok 4.6 at all, so its scores are checkable in a way GLM 5.3's are not yet.

Independent coverage cuts both ways, though, and it is worth being fair about the downside too. On AA-Omniscience, Artificial Analysis measured Grok 4.6 at a 65.7% non-hallucination rate, meaning that when it does not know an answer, it avoids inventing one about two times in three. That is in line with other frontier models, but it is the number to plan around for any customer-facing or research use, where a confident wrong answer reaches a real person. On xAI's own ten-row launch table, Grok 4.6 also loses several rows outright, most notably the terminal-heavy coding evaluations. Its real strength is knowledge work and turn efficiency, not raw agentic-coding dominance.

For the full row-by-row breakdown of Grok 4.6's launch table, see our Grok 4.6 benchmarks guide.

Where they were actually measured together

One independent test ran both models under the same harness, which makes it the closest thing to a real head-to-head in this comparison. On Quesma's Baba Is Bench, a game-based reasoning test, the two came out near-level.

Table 4: The one same-harness comparison available

Both models run by Quesma on the same harness. As of August 2026.

The two tie on the intro stage and sit one level apart on the harder Lake stage, with Grok 4.6 slightly ahead. It is a single independent test on a puzzle benchmark, not a broad verdict, but it is the one place both models faced identical conditions, and it lines up with the rest of the picture: these two are close.

Pricing: Grok 4.6 is cheaper per token, until you cross 200K tokens

Grok 4.6 looks cheaper than GLM 5.3 on paper, but its pricing has a cliff you need to plan around. Grok 4.6 lists at $2 input and $6 output per million tokens, with cached input at $0.50. That is roughly 60% below GPT-5.6 Sol and around 75% below Claude Opus 5 on output at list price, and it is the core of xAI's pitch.

The trap sits in the long-context band. Once a prompt reaches 200,000 tokens, xAI bills the entire request at the higher rate of $4 input and $12 output per million, not just the tokens above the threshold. A 500K context window with a toll booth at 200K is a different product from one without, and it matters most for exactly the long-running agents Grok 4.6 targets.

GLM 5.3's pricing is simpler and lower. Its API opened on August 18, 2026 at $1.40 input and $4.40 output per million tokens, with cached input at $0.26, matching GLM 5.2. There is no long-context cliff, so the rate holds whether a prompt is short or fills the 1M window. One nuance: 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.

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

Grok 4.6 pricing per xAI documentation. GLM 5.3 rate confirmed via Z.ai API, matching GLM 5.2, and live on OpenRouter.

For workloads that stay under 200K tokens, Grok 4.6 is genuinely cheap for its measured intelligence. For large-codebase or long-log workloads, the effective rate can more than double, so the fix is context hygiene: trim what you paste in, lean on caching, and price the long-context tier separately before you commit.

Also read our GLM 5.3 pricing guide for a full breakdown of what the long-context cliff means for your actual workload.

Context and openness: the two models point in opposite directions

GLM 5.3 and Grok 4.6 trade the two structural advantages between them: context size and deployment freedom. GLM 5.3 carries a 1M-token context window, double Grok 4.6's 500K, which is the largest gap between the two models on paper. For very large codebases or multi-document planning, that headroom is real, though most coding-agent workloads fit comfortably inside 500K.

Openness is the other axis, and it is unsettled for GLM 5.3. Z.ai released GLM 5.3 through its API and coding plan, and said the weights would follow roughly two weeks after the August 14 launch, after a safety review. GLM 5.2 shipped open weights within days, so GLM 5.3 broke that pattern. If self-hosting is the reason you are considering GLM 5.3, confirm the weights have actually shipped and check the license first.

Grok 4.6 is proprietary and makes no open-weight promise. It is available through the xAI API, Grok Build, Cursor, OpenRouter, Vercel, and Cloudflare, which is broad commercial access but no self-hosting. One more freshness caveat: xAI has signaled a larger Grok 4.7 within weeks of 4.6, so plan around the API endpoint rather than this specific version.

Verdict: which model should you choose?

Pick from the workload, then confirm on your own tasks before committing. These two models are close enough on shared benchmarks that the decision usually turns on context size, modality, pricing shape, and how much you value independent verification over raw claims.

Choose GLM 5.3 when:

• You need the larger 1M context, or want the option of open-weight self-hosting later.
• The work is defensive security and vulnerability discovery, where its CyberGym strength matters, and you can accept that its open-weight release was still pending at launch.

Also read our GLM 5.3 alternatives guide for what else is worth trying when neither model fits your workload.

Choose Grok 4.6 when:

• You need image input, independently verified intelligence, or broad commercial access.
• Your prompts stay under the 200,000-token pricing threshold most of the time.

Also read our Grok 4.6 alternatives guide for what else is worth trying when the 200K pricing cliff or lack of open weights becomes a dealbreaker.

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, cost per accepted result, and how often the model fabricates confidently. Keep model selection behind a stable interface so you can switch providers without rewriting your product.

From benchmarks to a shipped product with Emergent

The GLM 5.3 vs Grok 4.6 decision is close: GLM 5.3 for larger context, open-weight optionality, and security work, and Grok 4.6 for multimodal input, independently verified intelligence, and short-context cost efficiency. Verify the numbers that matter on your own tasks, and watch the GLM weights status and the Grok 200K pricing cliff, since both move the real cost.

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.