GLM-5.3 vs Claude: Pricing, Benchmarks, and Which One to Use (2026)

TL;DR

Last updated October 18, 2026. Prices re-read from Z.ai's and Anthropic's pricing docs that day. GLM-5.3 benchmark numbers are Z.ai-reported unless marked Artificial Analysis; Claude benchmark numbers are Anthropic-reported.

GLM-5.3 vs Claude comes down to which Claude. GLM-5.3 is Z.ai's open-weights coding model, an upgrade of GLM-5.2 on the same 753B mixture-of-experts base, at $1.40 per million input tokens and $4.40 per million output with a 1M context and reasoning that cannot be switched off. Anthropic's current lineup is Claude Fable 5.1 ($10/$50), Claude Opus 5.5 ($4/$20, released September 22, 2026), Claude Sonnet 5.5 ($2/$10), and Claude Haiku 5.5. On output tokens GLM-5.3 costs about 2.3x less than Sonnet 5.5, 4.5x less than Opus 5.5, and 11x less than Fable 5.1. Z.ai's own Code Bench puts GLM-5.3 at 31.4% at high effort, ahead of the older Opus 4.8 (29.5%) and behind Fable 5 (39.5%). Nobody has published a head-to-head against Opus 5.5 or Sonnet 5.5. Artificial Analysis scores GLM-5.3 45 on its Intelligence Index and flags it as very verbose. Claude adds image input and the Claude Code harness; GLM-5.3 adds open weights served by 26 API providers.

The Matchup

GLM-5.3 is one model. Claude is a ladder, and the comparison changes with the rung. Against Fable 5.1, GLM-5.3 is the budget challenger a tier below. Against Opus 5.5, it trades capability for a 4.5x lower output rate. Against Sonnet 5.5, the list-price gap shrinks to 2.3x on output, and Sonnet's cheaper cache reads close most of the rest on long cached contexts.

Sources: Z.ai pricing docs (GLM-5.3); Anthropic pricing and model docs (Claude); Morph pricing (morph-glm53-744b). Batch API requests are 50% off on Claude. GLM-5.3 is also bundled in Z.ai's GLM Coding Plan.

GLM-5.3 vs Claude Opus 5.5, Opus 5, and Opus 4.8

Z.ai picked Opus 4.8 as the model to beat. Its Code Bench chart shows GLM-5.3 at 31.4% at high effort using about 50K output tokens per task, against Opus 4.8 at 29.5% using 120K. A higher score at less than half the tokens is a strong efficiency claim, and at list prices it compounds: Opus 4.8 output costs $25/M against GLM-5.3's $4.40/M.

Anthropic moved the target on September 22, 2026. Claude Opus 5.5 costs $4/$20, 20% below Opus 5 and Opus 4.8 on both rates ($5/$25), and reads cache at $0.20/M. Anthropic reports 66.4% on Terminal-Bench 4.0 against 52.3% for Opus 5 and 57.9% for GPT-6 Astra, and 54.4% on FrontierCode v1.1. GLM-5.3 has no published number on either benchmark, so no source compares the two directly. The price gap is still wide: 4.5x on output and 2.9x on uncached input.

Terminal-Bench versions are not comparable with each other. On v3.0, GLM-5.2 scored 4.6, so a 28.3 is a large jump on a hard version.

GLM-5.3 vs Claude Fable 5.1 and Fable 5

Z.ai's own chart settles this one: Fable 5 scores 39.5% at max effort on Code Bench against GLM-5.3's 31.4%. Fable 5.1 replaced Fable 5 on September 1, 2026 at the same $10/$50 list price, with cache reads cut from $1.00 to $0.25 per million. Anthropic reports Fable 5.1 at 55.8% on Terminal-Bench 4.0, and notes that in its own use the gap between Opus 5.5 and Fable 5.1 is narrower than the benchmark margins suggest. On ExploitBench, Z.ai reports Claude Mythos 5 (the model family behind Fable) at 78.0% against GLM-5.3's 54.4%.

The useful question is whether the gap is worth 11x on output tokens. For work where a failed first attempt is cheap (a test suite verifies the patch, an agent loop retries), the cheaper model can lose on pass@1 and still win on solves per dollar. For work where the first answer must be right, or where the task needs image input, Fable 5.1 or Opus 5.5 is the pick.

GLM-5.3 vs Claude Sonnet 5.5

Sonnet 5.5 is the closest Claude to GLM-5.3 on price. It lists at $2/$10 per million tokens, 1.4x GLM-5.3 on input and 2.3x on output, and Anthropic reports 70.6% on Terminal-Bench 4.0, up from Sonnet 5's 10.3% and above Opus 5.5's 66.4% on that benchmark. Its cache reads cost $0.10 per million, so on a long agent session that re-reads the same repo context, the input side of the bill can come out lower than GLM-5.3's.

GLM-5.3 keeps two advantages here. Output stays 2.3x cheaper, which matters because GLM-5.3 and Sonnet 5.5 both reason before answering. And the weights are public, so the same model runs on 26 API providers or your own hardware. Sonnet 5.5 brings image input and first-party Claude Code support.

Pricing: The Worked Example

Sticker rates mislead in both directions: GLM-5.3's default max-effort reasoning inflates its output tokens, and Claude's cheap cache reads deflate its input. Take one long-context agent turn: 500K tokens of input (a repo slice plus a long trace) and 20K tokens of output.

Nominal token counts, cache-write charges excluded. Uncached, GLM-5.3 is 34% cheaper than Sonnet 5.5, 67% cheaper than Opus 5.5, and 87% cheaper than Fable 5.1 on this turn. With 90% of input cached, its lead over Sonnet 5.5 narrows to about 20%, because Sonnet reads cache at $0.10/M against GLM-5.3's $0.26/M. A hard turn where GLM-5.3 reasons well past 20K output tokens narrows it further.

For the full Anthropic rate card, see Anthropic API pricing; for GLM-5.3 against its own predecessor, see the GLM-5.3 guide.

Verbosity and Token Efficiency

Z.ai's chart and Artificial Analysis measure different settings and reach opposite conclusions. On Z.ai's Code Bench, GLM-5.3 at high effort scores 31.4% at roughly 50K output tokens per task against Opus 4.8's 29.5% at 120K. On Artificial Analysis's suite, GLM-5.3 at max effort generated about 210M output tokens against a 130M median, which AA rates as very verbose.

The effort setting reconciles them. GLM-5.3 supports low, high, and max reasoning effort, max is the default, and reasoning cannot be disabled. At $4.40/M output the extra tokens add up across an agent loop, so set reasoning_effort to high for routine work. Artificial Analysis measured 92.9 output tokens per second on Z.ai's own API, and the fastest third-party endpoint it tracks runs at 733.4 tokens per second, so provider choice moves wall-clock time by close to 8x.

Cyber Capability and Safety

GLM-5.3's one claimed win over the Claude line sits here. Z.ai reports CyberGym at 84.5%, up from GLM-5.2's 77.2% and ahead of Claude Mythos 5's 83.8% and GPT-5.6 Sol's 83.6%. Z.ai also says the model found 2,436 vulnerabilities in testing on real-world targets, 1,097 of them medium or high severity. On ExploitBench the frontier still leads: 54.4% for GLM-5.3 against Mythos 5's 78.0%.

Anthropic keeps its most cyber-capable tier, Claude Mythos 5.1, in limited availability, and ships Fable 5.1 at the same price for general use. Z.ai took the opposite route and published the GLM-5.3 weights. For authorized security work, GLM-5.3 is tuned for vulnerability research and priced for triage at volume; Claude pairs comparable CyberGym results with a safety framework that compliance reviews already know.

Ecosystem and Availability

Model quality is half of agent performance; the harness and the supply chain are the other half. Claude Code is Anthropic's first-party agent, and Opus 5.5 and Fable 5.1 are tuned to drive it. Claude also runs on Amazon Bedrock, Google Cloud, and Microsoft Foundry. GLM-5.3 works in Claude Code and OpenCode through Z.ai's Anthropic-format endpoint, and Morph serves the same Anthropic Messages API for morph-glm53-744b. GLM-5.3 has no image input, which rules it out of any agent leg that reads screenshots or diagrams.

Z.ai published zai-org/GLM-5.3 on Hugging Face on August 25, 2026 under a custom GLM-5.3 License, MIT-like plus a security-review clause for model-as-a-service operators above $10 billion in annual revenue. Artificial Analysis now lists 26 API providers for it, with the lowest blended price at $0.41 per million tokens. Morph serves GLM-5.3 as morph-glm53-744b at $1.19/M input, $0.1955/M cached, and $6/M output with the full 1M context. See Morph Open Source Models and the GLM 5 family guide.

GLM-5.3 vs Claude: Pros and Cons

Verdict by Workload

Pick per workload; no single model wins all of these.

• Hardest agentic coding, vision, compliance: Claude Opus 5.5 at $4/$20, or Fable 5.1 at $10/$50 when Opus 5.5 at high effort still falls short, which is the order Anthropic recommends.
• Long cached agent sessions on a budget: Claude Sonnet 5.5. Its $0.10/M cache reads make it within about 20% of GLM-5.3 on a mostly cached turn, with image input included.
• High-volume agentic coding where retries are cheap: GLM-5.3. At $4.40/M output a failed attempt costs little. Set reasoning effort to high.
• Security review and vulnerability triage at volume: GLM-5.3, for authorized work. The cyber post-training is its clearest differentiation and the price fits triage-scale usage.
• Open weights served today: GLM-5.3 on Morph (morph-glm53-744b, $1.19/$6, 1M context), with GLM-5.2 as the MIT-licensed fallback if the GLM-5.3 License does not fit.
• Everything at once: route by task. Keep Claude for the hard tier and send the high-volume tier to a cheaper model. See LLM routing.

For the full GLM-5.3 fact base, see GLM-5.3: benchmarks, pricing, cyber capabilities. For the previous round of this matchup, see GLM-5.2 vs Claude Opus. For the other open-lineage flagship against the same lineup, see Kimi K3 vs Claude. For every GLM 5 version in one table, see the GLM 5 family guide.

FAQ

Is GLM-5.3 better than Claude?

It depends on which Claude. Z.ai's Code Bench puts GLM-5.3 at 31.4% at high effort, ahead of Opus 4.8 (29.5%) and behind Fable 5 (39.5%), and both of those Claude models are now legacy. Anthropic's current Opus 5.5 scores 66.4% on Terminal-Bench 4.0 and Fable 5.1 scores 55.8%; GLM-5.3 has no score on that version. Artificial Analysis gives GLM-5.3 an Intelligence Index of 45, second of 120 comparable open-weights models. Treat GLM-5.3 as a cheaper Opus-4.8-class model and Opus 5.5 as the stronger current pick.

How much cheaper is GLM-5.3 than Claude?

GLM-5.3 is $1.40/$4.40 per M tokens against Opus 5.5's $4/$20 (2.9x and 4.5x), Fable 5.1's $10/$50 (7x and 11x), and Sonnet 5.5's $2/$10 (1.4x and 2.3x). Cached input reverses the order: Sonnet 5.5 reads cache at $0.10/M and Opus 5.5 at $0.20/M, below GLM-5.3's $0.26/M. On a mostly cached 500K-input turn, GLM-5.3 costs about $0.28 against $0.35 for Sonnet 5.5.

GLM-5.3 vs Claude Opus 5.5: which should I use?

Opus 5.5 for the hardest agentic coding and image input: 66.4% on Terminal-Bench 4.0 and 54.4% on FrontierCode v1.1 per Anthropic, at $4/$20. GLM-5.3 for high-volume coding where retries are cheap, at a 4.5x lower output rate. Z.ai benchmarked against Opus 4.8, so no direct comparison exists yet.

Does GLM-5.3 beat Claude Opus 4.8?

On Z.ai's internal Code Bench, yes: a higher score at less than half the output tokens. That chart is vendor-internal and unreplicated. Opus 4.8 is now legacy at $5/$25, and Opus 5.5 costs less and scores higher.

Does GLM-5.3 beat Claude Fable 5.1?

No. Z.ai's own chart has Fable 5 at 39.5% against GLM-5.3's 31.4%, and Fable 5.1 replaced Fable 5 at the same price. CyberGym is the one benchmark where GLM-5.3 edges the Claude line, per Z.ai.

Can GLM-5.3 do vision like Claude?

No. GLM-5.3 is text-only, and every current Claude model accepts image input. Z.ai's GLM-5.3-Flash added native vision, and Kimi K3 is the larger open-lineage model with vision.

Is GLM-5.3 open weights?

Yes, since August 25, 2026, but not under MIT. zai-org/GLM-5.3 is public on Hugging Face under a custom GLM-5.3 License: MIT-like, plus a Z.ai security review for model-as-a-service operators above $10 billion in annual revenue. GLM-5.2 stays MIT. Morph serves GLM-5.3 as morph-glm53-744b.

Should I switch from Claude to GLM-5.3?

Not wholesale. Route by task: Claude Opus 5.5 or Fable 5.1 for the hardest reasoning, vision, and compliance-sensitive work; GLM-5.3 for high-volume cost-sensitive coding. Check the custom GLM-5.3 License against your use.