GLM-5.1 Review (2026): Better Than Claude Opus for Coding?

Date August 2, 2026 Read time 7 min

GLM-5.1 launched in April 2026 as Z.AI’s answer to models like Claude Opus 4.6 and GPT-5.4. Unlike those models, GLM-5.1 is open-weight and released under the MIT license, meaning developers can self-host it, fine-tune it, or run it through third-party providers. If you’re mainly looking for a coding model, that’s a much bigger selling point than benchmark scores alone.

Is GLM-5.1 good for coding?

Coding is where GLM-5.1 stands out. On SWE-Bench Pro, it scores 58.4, narrowly ahead of Claude Opus 4.6 (57.3) and GPT-5.4 (57.7). Since SWE-Bench Pro measures real software engineering tasks instead of isolated coding questions, that lead is more meaningful than a small gain on synthetic benchmarks.

The picture is similar across other coding benchmarks. Claude Sonnet 4.6 edges ahead on SWE-bench Verified, but only by a small margin (79.6% vs. 77.8%). GLM-5.1 also performs exceptionally well on CyberGym, outperforming Claude Opus 4.6. Taken together, the results suggest it has reached the same tier as today’s leading coding models rather than simply being a strong open-source alternative.

One of GLM-5.1’s more interesting features is its ability to work autonomously for up to eight hours on a single task. Instead of waiting for constant prompts, it can plan, execute, test, and refine its work over long coding sessions. Whether that’s useful depends on your workflow, but it’s a capability that few competing models currently advertise.

Where does GLM-5.1 fall short?

Outside of coding, GLM-5.1 is easier to beat. Claude Opus 4.6 performs much better on general knowledge and web browsing benchmarks, while GPT-5.4 remains stronger for advanced mathematics and reasoning. If you’re looking for a single model that excels at everything, GLM-5.1 isn’t there yet.

Speed is another downside. Artificial Analysis measures GLM-5.1 at around 64 tokens per second, slightly below the current median. More importantly, users frequently report that its reasoning mode is extremely verbose, sometimes generating thousands of thinking tokens before producing an answer. That increases both response times and API costs, especially on simpler coding tasks.

Even after accounting for its higher token usage, GLM-5.1 is still significantly cheaper than Claude Opus 4.6. Depending on the provider, output tokens cost roughly five to eight times less. Unless your workloads consistently trigger extremely long reasoning chains, its lower pricing remains a meaningful advantage.

Pros and cons

Pros

Leads Claude Opus 4.6 and GPT-5.4 on SWE-Bench Pro (58.4), a demanding real-world coding benchmark
API pricing as low as $0.952 per million input tokens and $2.992 per million output tokens on OpenRouter, roughly a fifth of Opus 4.6’s rate
Open-weight under an MIT license, allowing self-hosting and fine-tuning
Can autonomously run a single task for up to 8 hours, useful for large refactors or long test-and-fix loops
GLM Coding Plan subscription runs $3-$30/month versus $100-$200/month for Claude Max

Cons

Trails Claude Opus 4.6 sharply on general knowledge (52.3 vs 76.2) and web browsing (68% vs 83.7% on BrowseComp)
Slower than the median model at 64 tokens per second
Can be excessively verbose in its reasoning mode, burning tens of thousands of tokens on simple decisions
Self-hosted performance reported as “quite slow” without full DSA support
Falls behind GPT-5.4 and Opus 4.6 on pure reasoning and math benchmarks like AIME

Who should use GLM-5.1?

Best for:

Engineering teams running agentic coding workflows – refactors, test generation, repo-scale bug fixes – where SWE-Bench-style task performance matters more than general knowledge
Developers who want to self-host or fine-tune an open-weight model rather than depend on a closed API
Budget-conscious teams or individuals evaluating the GLM Coding Plan against Claude Max for routine coding assistance
Long-running autonomous agent setups that can tolerate an 8-hour unattended task loop

Not ideal for:

Users who need strong general knowledge or web research capability alongside coding
Interactive, latency-sensitive applications, given the model’s below-median token throughput
Anyone running mathematically heavy reasoning workloads where GPT-5.4’s AIME lead is decisive
Self-hosters without hardware suited to a 754B-parameter MoE model, given reported slowness even at that scale

Is GLM-5.1 worth it?

For coding-centric use, yes. GLM-5.1’s SWE-Bench Pro lead over both Opus 4.6 and GPT-5.4, combined with API pricing roughly a fifth of Opus 4.6’s output cost ($2.992 vs $25.00 per million tokens), makes it a strong economic choice for teams whose primary workload is software engineering. The GLM Coding Plan’s $3-$30 monthly tiers versus Claude Max’s $100-$200 reinforce that this is a genuine budget alternative, not just a marginally cheaper one.

If coding is your primary workload, GLM-5.1 is easy to recommend. It delivers top-tier benchmark performance while costing considerably less than Claude Opus 4.6. If you need stronger reasoning, web research, or broader general-purpose performance, Claude and GPT-5.4 are still the safer choices. For everyone else, GLM-5.1 offers one of the best performance-per-dollar ratios available today.

Specs and benchmark summary

Metric	GLM-5.1	Claude Opus 4.6	GPT-5.4
Parameters	754B MoE, 40B active	–	–
Context window	202,752 tokens	1M tokens	–
Max output	131,072 tokens	–	–
SWE-Bench Pro	58.4	57.3	57.7
SWE-bench Verified	77.8%	–	–
CyberGym	68.7	66.6	–
BrowseComp	68%	83.7%	–
AIME	–	–	98.7%
API price (in/out per 1M)	$0.952 / $2.992	$5.00 / $25.00	–
Output speed	64 tok/s	–	–
License	MIT (open weight)	Closed	Closed

GLM-5.1 proves that open-weight models are catching up quickly. It isn’t the best model for every task, but for software engineering it competes directly with the biggest proprietary models while costing far less. If coding is your priority, it’s one of the easiest recommendations in 2026.

FAQ

Is GLM-5.1 better than Claude Opus 4.6 for coding? On SWE-Bench Pro, GLM-5.1 scores 58.4 versus Opus 4.6’s 57.3, and it also leads on CyberGym (68.7 vs 66.6). On SWE-bench Verified, Claude Sonnet 4.6 is slightly ahead (79.6% vs 77.8%), a gap small enough to be effectively a tie. Overall, GLM-5.1 matches or beats Opus 4.6 on coding-specific benchmarks.

How much cheaper is GLM-5.1 than Claude Opus 4.6? GLM-5.1’s API pricing on OpenRouter is $0.952 per million input tokens and $2.992 per million output tokens, compared to $5.00 and $25.00 for Opus 4.6 – roughly a fifth of the output cost. The GLM Coding Plan subscription also runs $3-$30/month versus $100-$200/month for Claude Max.

Why is GLM-5.1 slower than other models? Artificial Analysis clocks GLM-5.1 at 64 tokens per second, below the median of 65 among tracked models, and notes it is “particularly expensive… slower than average and somewhat verbose.” Users have also reported the model burning large numbers of output tokens on internal reasoning before producing a final answer, which adds to both latency and cost.

Is GLM-5.1 open source? Yes. GLM-5.1 is distributed under an MIT license, which means it can be self-hosted, fine-tuned, or run through third-party inference providers rather than being locked to a single vendor’s API.

What are GLM-5.1’s main weaknesses? It trails Claude Opus 4.6 substantially on general knowledge (52.3 vs 76.2) and web-browsing tasks (68% vs 83.7% on BrowseComp), and it falls behind GPT-5.4 on math reasoning benchmarks like AIME (98.7%). It is also slower and more verbose than its benchmark scores might suggest.