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

TL;DR

Last updated August 21, 2026, one week after GLM-5.3 launched. Nearly every GLM-5.3 number here is Z.ai-reported; the independent datapoints are called out explicitly. Claude's numbers are independently replicated.

GLM-5.3 is Z.ai's August 14, 2026 upgrade of GLM-5.2: the same 753B mixture-of-experts base, all gains from scaled post-training, priced at $1.40/M input and $4.40/M output with a 1M context and always-on reasoning. Claude Fable 5 ($10/$50) is the most capable model Anthropic ships; Claude Opus 4.8 ($5/$25) is the workhorse below it. Z.ai's internal Code Bench chart puts GLM-5.3 at 31.4% using about 50K output tokens per task, ahead of Opus 4.8's 29.5% at 120K tokens and behind Fable 5's 39.5%. The one independent check so far, Artificial Analysis, scores it 60 on the Intelligence Index, rank 8 of 182, and flags it as very verbose. GLM-5.3's output rate is about 11x below Fable 5's; what Claude sells is verified top-tier capability, vision, the Claude Code harness, and multi-provider availability against GLM-5.3's single gated endpoint.

The Matchup

GLM-5.3 is one model. Claude is a ladder, and the comparison changes depending on the rung. Against Fable 5, GLM-5.3 is the budget challenger a tier below. Against Opus 4.8, it is a direct capability rival at one-fifth the output rate. Against Sonnet 5, the price gap nearly closes and the verification gap remains.

Sources: Z.ai pricing docs (GLM-5.3); Anthropic pricing docs (Claude); Morph pricing (GLM-5.2). GLM-5.3 is also bundled in the GLM Coding Plan at $18, $72, or $160 per month. Fable 5, Opus 4.8, and Sonnet 5 bill the full 1M context flat, with no long-context surcharge.

GLM-5.3 vs Claude Fable 5

This one has a clear answer, and Z.ai's own chart gives it: Fable 5 is the stronger model. On Z.ai's internal Code Bench, GLM-5.3 scores 31.4% and Fable 5 scores 39.5% at max effort. On ExploitBench, the closed frontier still leads: 78.0% for Claude Mythos 5 (the same underlying model as Fable 5, without Fable's added dual-use safeguards) against GLM-5.3's 54.4%. And Fable 5's headline coding scores are the replicated kind: 95.0 on SWE-bench Verified and 84.3 on Terminal-Bench 2.1, reproduced across independent harnesses. GLM-5.3 has no published SWE-bench Verified number from anyone.

The question is not whether GLM-5.3 matches Fable 5. It is whether the gap is worth 11x on output tokens. Fable 5 costs $10/$50 per M; GLM-5.3 costs $1.40/$4.40. 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 vision input GLM-5.3 does not have, Fable 5 is the pick and the price is the price.

Terminal-Bench versions differ: GLM-5.3 reports the harder v3.0 (where GLM-5.2 scores 4.6), Fable 5 reports v2.1. The two numbers are not comparable; read each against its own version. CyberGym and ExploitBench figures for the Claude side are reported by Z.ai for Claude Mythos 5.

GLM-5.3 vs Claude Opus 4.8

This is the interesting fight, because it is the one GLM-5.3 claims to win. Z.ai's internal Code Bench chart shows GLM-5.3 at 31.4% 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, so on Z.ai's numbers the per-task output bill differs by more than 10x.

The counterweights are verification and everything around the model. The Code Bench chart is Z.ai's own harness scoring Z.ai's own model; no third party has replicated it. Opus 4.8's scores are replicated, and it ships with operational knobs GLM-5.3 lacks: batch mode at $2.50/$12.50 for offline work, fast mode for latency-sensitive loops, image input, and the Claude Code harness it is tuned to drive. Opus 4.8 also runs on multiple providers, while GLM-5.3 has exactly one upstream until the weights open. If the efficiency claim replicates, GLM-5.3 is the better per-dollar coding model of the two. As of one week in, that is a claim, not a result.

Pricing: The Worked Example

Sticker rates mislead in both directions here: GLM-5.3's always-on reasoning inflates its output tokens, and Claude's newer tokenizer inflates its input tokens. So take a concrete long-context agent turn: 500K tokens of input (a repo slice plus a long trace) and 20K tokens of output.

Nominal token counts, no tokenizer or reasoning adjustment. GLM-5.3 is about 74% cheaper than Opus 4.8 and 87% cheaper than Fable 5 on this turn. Adjust for Claude's ~30% higher token count on the same input text and the gap widens; adjust for GLM-5.3's max-effort reasoning emitting far more than 20K output on a hard turn and it narrows. Caching moves all three down: GLM-5.3 cached input is $0.26/M, Opus 4.8 $0.50/M, Fable 5 $1.00/M.

The structural picture: both sides bill the full 1M context flat, so this is a per-token-rate gap, not a context-surcharge story. GLM-5.3 is cheaper at every tier of the Claude ladder except intro-priced Sonnet 5 on input, and the discount buys mostly vendor-verified capability. For the raw 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

These two facts point in opposite directions and both are real. On Z.ai's Code Bench chart, GLM-5.3 is the efficiency story: 31.4% at roughly 50K output tokens per task against Opus 4.8's 29.5% at 120K. On Artificial Analysis's independent suite, GLM-5.3 is the verbosity outlier: about 170M output tokens across the evals against a 72M median, one of the most verbose models tracked.

The reconciliation is the effort setting. GLM-5.3 runs reasoning always on with low, high, and max levels, and max is the default. Z.ai's chart reflects its harness; the AA measurement reflects defaults. At $4.40/M output the verbosity compounds quietly across an agent loop, so cap effort at high for routine work. Claude's reasoning budget is caller-controlled, and it summarizes rather than emitting full traces. Artificial Analysis also measured GLM-5.3 at 93.2 output tokens per second, so the verbosity costs wall-clock time as well as dollars.

Cyber Capability and Safety

GLM-5.3's launch tagline was "Built to Code. Ready for Cyber Defense," and its one claimed win over the Claude line sits here. Z.ai reports CyberGym at 84.5%, edging Claude Mythos 5's 83.8% and GPT-5.6 Sol's 83.6%. Applied, Z.ai says the model found 2,436 vulnerabilities across 269 open-source projects, 1,097 rated critical or high, the oldest dating to 1981, with disclosures running through a public ledger at cvd.z.ai (53 public CVEs at launch). On ExploitBench the frontier still leads: 54.4% for GLM-5.3 against Mythos 5's 78.0%.

The Claude side of this story is structural. Fable 5 is the generally available version of the Mythos-class model, carrying additional safeguards for dual-use capabilities; Mythos 5 itself goes only to approved organizations. Z.ai took the mirrored approach on weights rather than the API: it gated the GLM-5.3 checkpoint at launch for safety hardening precisely because of the cyber capability. For teams doing authorized security work, the practical read is that GLM-5.3 is tuned hard for vulnerability research and priced for volume triage, while Claude pairs near-equal CyberGym capability with a published 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 with a deep tooling ecosystem, and Fable 5 and Opus 4.8 are tuned to drive it. Claude also runs on multiple providers, so a regional outage is a reroute rather than an incident. GLM-5.3 works in Claude Code, OpenCode, and ZCode through Z.ai's Anthropic-format endpoint, which makes trying it cheap. Running production on it is a different bet: OpenRouter lists z-ai/glm-5.3 with a single provider because the weights are gated, so every request funnels through one upstream with no failover. GLM-5.3 also has no vision input, which rules it out of any agent leg that reads screenshots or diagrams.

The availability picture changes when the weights open. Z.ai promised them roughly two weeks after launch, pending safety hardening; the Hugging Face repo exists but returns 401 and the license is unconfirmed. Morph serves GLM-5.2 today as morph-glm52-744b at $0.8415/M input and $3.1365/M output on codegen-tuned kernels with the full 1M context, the open-weight route to this model family right now. When the GLM-5.3 weights land, the same serving path applies. See Morph Open Source Models.

GLM-5.3 vs Claude: Pros and Cons

Verdict by Workload

One week after launch, the honest call is per workload, not a single winner.

• Hardest reasoning, verified top tier, vision, compliance: Claude Fable 5. It leads the replicated benchmarks and Z.ai's own chart alike, and it drives the mature harness. You pay $10/$50 for certainty.
• Verified near-top capability with cost knobs: Claude Opus 4.8 at $5/$25, with batch at $2.50/$12.50. GLM-5.3 claims to beat it per token; until that replicates, Opus 4.8 is the settled pick at this tier.
• High-volume agentic coding where retries are cheap: GLM-5.3. At $1.40/$4.40 a failed attempt costs little, and the efficiency claim only has to be roughly true for the per-dollar math to favor it. Cap reasoning effort at 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-5.2 on Morph (morph-glm52-744b, $0.8415/$3.1365, 1M context). GLM-5.3 joins when its weights open.
• 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 (benchmark deltas, the cyber program, weights status), 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.

FAQ

Is GLM-5.3 better than Claude?

It depends on which Claude. Z.ai's own chart puts GLM-5.3 ahead of Opus 4.8 on token efficiency (31.4% at ~50K output tokens per task vs 29.5% at 120K) and behind Fable 5 (39.5%). The one independent datapoint, Artificial Analysis, ranks it 8 of 182 with an Intelligence Index of 60. Claude's scores are independently replicated; GLM-5.3's are one week old and mostly vendor-reported. Fable 5 holds the verified top tier; GLM-5.3 is a credible Opus-tier challenger at one-fifth the price.

How much cheaper is GLM-5.3 than Claude?

GLM-5.3 is $1.40/$4.40 per M tokens against Fable 5's $10/$50 (about 7x and 11x) and Opus 4.8's $5/$25 (about 3.6x and 5.7x). Adjust for GLM-5.3's verbosity (170M eval output tokens vs a 72M median, per Artificial Analysis) and Claude's ~30% heavier tokenizer, and compare on cost per completed task.

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's scores are replicated and it brings batch pricing, vision, and the Claude Code harness. Treat GLM-5.3 as a plausible Opus-tier model pending independent confirmation.

Can GLM-5.3 do vision like Claude?

No. GLM-5.3 is text-only. Every current Claude model accepts image input. If your agents read screenshots or diagrams, Claude covers that leg and GLM-5.3 does not.

Is GLM-5.3 open weights?

Not yet. The Hugging Face repo is gated, Z.ai promised weights roughly two weeks after the August 14 launch, and the license is unconfirmed. GLM-5.2 is the open-weight option today (MIT), served on Morph as morph-glm52-744b.

Should I switch from Claude to GLM-5.3?

Not wholesale. Route by task: Claude for the hardest reasoning, vision, and compliance-sensitive work; GLM-5.3 for high-volume cost-sensitive coding once its numbers hold up. The single gated endpoint is the production risk to price in until the weights open.