GLM 5.2 vs Claude Opus 5: Which AI Model Is Better for Coding?

Date August 3, 2026 Read time 8 min

GLM 5.2 and Claude Opus 5 target the same audience: developers who need powerful coding and long-context AI models. Both support a 1-million-token context window, but they take very different approaches. Opus 5 focuses on maximum performance, while GLM 5.2 aims to deliver most of that capability at a much lower cost.

Performance: Opus 5 leads, but the gap is narrower on some tasks than others

Claude Opus 5 is the stronger model overall, particularly on difficult coding and agentic software engineering tasks. Its biggest advantage isn’t simple code generation rather handling long, multi-step workflows where accuracy matters more than speed.

Independent testing suggests GLM 5.2 still trails Anthropic’s flagship models on demanding coding tasks. Although most published comparisons use Opus 4.8 rather than Opus 5, the gap is likely to remain because Opus 5 improved further on the same benchmarks.

Comparing GLM 5.2 directly against Opus 4.8 (the predecessor Anthropic used as its own baseline), independent testing found GLM 5.2 trailing by 7 points on SWE-bench Pro — described in that review as “the difference you are paying the Opus premium for” — and that gap widens further on harder, long-horizon coding tasks. Since Opus 5 improved substantially over Opus 4.8 on exactly those long-horizon tasks, the practical gap between GLM 5.2 and the current Opus 5 is likely larger than the 7-point figure suggests, though no findings directly benchmark GLM 5.2 against Opus 5 on Frontier-Bench.

On long-context retrieval — pulling correct facts out of very large documents — a Braintrust benchmark found GLM-5.2 “slightly behind” Opus 4.8, a modest gap rather than a decisive one. In a hands-on test, building a 3D WebGL game in one shot, Opus finished in half the time and produced cleaner, more correct output than GLM-5.2.Overall, the trend is consistent: GLM 5.2 comes surprisingly close on many workloads, but Opus 5 maintains a clear edge whenever tasks become more complex or require sustained reasoning.

Anthropic kept Opus 5 at the same API pricing as Opus 4.8 despite improving performance, while Z.ai continued to compete mainly on price. As a result, the decision is fairly straightforward: Opus offers higher accuracy, while GLM delivers much better value for money.

Cost: GLM 5.2 is substantially cheaper, and the gap holds up in practice

If price per token is the deciding factor, GLM 5.2 wins by a wide margin. Z.ai’s official pricing lists GLM-5.2 at $1.40 per million input tokens and $4.40 per million output tokens, while OpenRouter hosts show it as low as $0.6993 input / $2.198 output per million tokens depending on provider. Opus 5, by contrast, is priced at $5 per million input tokens and $25 per million output tokens — unchanged from Opus 4.8, and roughly 3.5x to over 11x GLM 5.2’s rate depending on which GLM price point is used and whether input or output tokens dominate the workload.

The lower pricing also shows up in real workloads. Even after accounting for GLM 5.2’s slightly lower accuracy, independent testing found it remained significantly cheaper per successful result, making it an attractive option for high-volume applications. GLM 5.2 also responds much faster, producing nearly twice as many tokens per second as Opus 5 in comparable tests. That difference is noticeable in chat applications and interactive coding assistants where latency affects the user experience.

Speed, context, and what each model can actually take as input

GLM 5.2 is the faster model in raw throughput, generating 99.0 tokens per second in non-reasoning mode versus Opus 5’s 50.6 tokens per second at medium adaptive-reasoning effort — roughly double the speed, which matters for latency-sensitive applications like chat interfaces or interactive coding assistants.

Both models now match on the headline context spec: GLM 5.2 offers a 1,048,576-token context window with 128,000 max output tokens, while Opus 5 offers the same 1M context window with 128k max output on the synchronous API, extendable to 300k output tokens via a batch-API beta header. In practical terms, a 1M-token window is large enough to hold an entire mid-sized codebase or a lengthy legal contract set in a single conversation, and neither model has an advantage here on paper.

One area where Opus 5 clearly pulls ahead is multimodal input. It can understand images alongside text, while GLM 5.2 is limited to text. If your workflow involves screenshots, diagrams, or UI reviews, that alone may justify choosing Opus.

Pros and cons

Pros (GLM 5.2)

Roughly 3.5x to 11x cheaper per million tokens than Opus 5, depending on host and token type
Faster raw generation at 99.0 tokens/second versus Opus 5’s 50.6
MIT-licensed and open-weight, so it cannot be retired or access-restricted the way a closed model can
Matches Opus on the 1M-token context window spec, and is only “slightly behind” on long-context retrieval accuracy
76-86% cheaper per trace/correct answer on real long-context benchmarks

Cons (Opus 5)

No image or vision input; text-only, unsuitable for visual verification tasks
Trails by 7 points on SWE-bench Pro against even the older Opus 4.8, a gap that widens on harder tasks
Took twice as long and produced a less polished result in a one-shot game-building test against Opus
Proprietary licensing limits how it can be redistributed or self-hosted, unlike GLM’s MIT license

Who should use GLM 5.2 vs Opus 5?

Best for GLM 5.2:

Startups and teams running high-volume inference where per-token cost dominates the budget
Long-context document or knowledge-base retrieval pipelines where a small accuracy gap is an acceptable trade for large savings
Organizations that need open-weight models for self-hosting, compliance, or vendor-independence reasons
Latency-sensitive applications where the roughly 2x speed advantage matters

Best for Opus 5:

Teams building autonomous coding agents where SWE-bench-style correctness on complex, multi-step tasks is critical
Workflows requiring image or screenshot input alongside text
Applications where the cost of a wrong answer (a bad deploy, a broken agent action) far outweighs the token bill

Not ideal for GLM 5.2: any workflow requiring visual input, or agentic coding tasks with long, difficult multi-step horizons where the accuracy gap compounds.

Not ideal for Opus 5: high-volume, cost-sensitive batch workloads where GLM 5.2’s accuracy is “good enough” and the price difference is the deciding factor.

Is GLM 5.2 or Opus 5 worth it?

Opus 5 is worth its price for teams whose product depends on getting hard, multi-step agentic tasks right — the 43.3% Frontier-Bench score, more than double Opus 4.8’s, indicates real progress on exactly the tasks where cheaper models fall furthest behind. For those users, the $5/$25 per million token pricing is a cost of doing business, especially with caching discounts applied.

GLM 5.2 is worth it for nearly everyone else. A 7-point gap on SWE-bench Pro and being “slightly behind” on long-context retrieval are real but modest shortfalls, and they arrive alongside a price roughly a third to a tenth of Opus 5’s, open licensing, and faster generation. Unless a workload specifically needs vision input or pushes into the hardest long-horizon agentic territory, GLM 5.2’s combination of price and openness makes it the more defensible default choice.

Specs and benchmark summary

	GLM 5.2	Claude Opus 5
Release date	June 16, 2026	July 24, 2026
Architecture	753B parameter MoE, MIT license	Proprietary, $5/$25 per M tokens unchanged from Opus 4.8
Context window	1,048,576 tokens	1M tokens
Max output	128,000 tokens	128k standard, 300k via batch API
Input price	$1.40/M ($0.70/M on some OpenRouter hosts)	$5/M
Output price	$4.40/M ($2.20/M on some hosts)	$25/M
Speed	99.0 tok/s (non-reasoning)	50.6 tok/s (medium reasoning)
SWE-bench Verified/Pro	trails Opus 4.8 by 7 points on SWE-bench Pro	96.0% / 79.2%
Frontier-Bench v0.1	not directly tested	43.3% (vs Opus 4.8’s 18.7%)
Multimodality	text-only	text and image

FAQ

Is GLM 5.2 as good as Claude Opus 5 for coding? Not quite. GLM 5.2 trails Opus 4.8 (Opus 5’s predecessor) by 7 points on SWE-bench Pro, and since Opus 5 improved significantly on hard agentic tasks over Opus 4.8, the real-world gap against Opus 5 is likely larger. GLM 5.2 is competitive on routine coding but consistently a step behind on complex, multi-step work.

How much cheaper is GLM 5.2 than Opus 5? GLM 5.2 costs roughly 3.5x to over 11x less per million tokens than Opus 5, depending on the hosting provider and whether input or output tokens dominate. Opus 5 is priced at $5/$25 per million tokens, while GLM 5.2 ranges from about $0.70-$1.40 per million input tokens and $2.20-$4.40 per million output tokens.

Does GLM 5.2 support image input? No. GLM-5.2 is text-only, while Opus 5 accepts both text and images. This makes GLM 5.2 unsuitable for workflows that require reading screenshots, reviewing UI mockups, or visually verifying output.

Which model has a bigger context window, GLM 5.2 or Opus 5? They’re effectively tied. GLM 5.2 offers a 1,048,576-token context window with 128,000 max output tokens, and Opus 5 offers a matching 1M-token context window with 128k max output (extendable to 300k via a batch-API beta header).

Is GLM 5.2 or Opus 5 faster? GLM 5.2 is faster in raw throughput, generating 99.0 tokens per second in non-reasoning mode versus Opus 5’s 50.6 tokens per second at medium reasoning effort — roughly double the speed, which benefits latency-sensitive applications.