GLM vs Claude: coding, reasoning and price

Choose GLM-5.3 to cut the example request to $0.0184 on Novita AI from $0.0600 for Claude Opus 5.5, about 3.3× less; choose Claude when reasoning and agentic coding matter most, where it leads HLE by 19.1 pp (61.4% vs 42.3%) and Terminal-Bench 4.0 by 17.7 pp.

Updated · 4 sources · Methodology

DimensionWinnerMarginEvidence
Coding benchmarkClaude Opus 5.517.7 ppTerminal-Bench 4.0: Claude 59.6% vs GLM-5.3 41.9%12
Reasoning benchmarkClaude Opus 5.519.1 ppHLE: Claude 61.4% vs GLM-5.3 42.3%12
Output speed (tokens/s)GLM-5.31.4 tokens/sGLM-5.3 98.2 vs Claude 96.8 tokens/s12
Estimated request costGLM-5.3$0.0416GLM-5.3 $0.0184 vs Claude $0.060034

Coding and reasoning benchmarks

MetricGLM-5.3 (Max)Claude Opus 5.5 (Max)
AA · Terminal-Bench 4.01241.9%59.6%
AA · HLE1242.3%61.4%
AA · SciCode1259.0%66.9%

Performance

MetricGLM-5.3 (Max)Claude Opus 5.5 (Max)
Output speed (tokens/s)1298.296.8
Time to first answer token (s)1222.7676.8

Prices and request cost

MetricGLM-5.3Claude Opus 5.5
Input / 1M tokens34$1.40$4.00
Output / 1M tokens34$4.40$20.00
Context window (tokens)341,048,5761,000,000
Cost example: 10,000 input + 1,000 billed output tokens
GLM-5.3$0.0184
Claude Opus 5.5$0.0600

When each option fits

GLM-5.3

  • For budget-bound agent workloads, GLM-5.3 on Novita AI costs $1.40 input and $4.40 output per 1M tokens, about 3.3× less than Claude per example request.34
  • GLM-5.3’s smallest gap to Claude is SciCode, 59.0% versus 66.9% (7.9 pp), so it fits scientific coding better than terminal agents.12
  • For long inputs, Novita AI lists a 1,048,576-token context for GLM-5.3, slightly above Claude’s 1,000,000.34

Claude Opus 5.5

  • For reasoning-heavy work, Claude’s 61.4% on HLE is 19.1 pp above GLM-5.3’s 42.3%, its largest lead on this page.12
  • For terminal-based coding agents, Claude leads Terminal-Bench 4.0 by 17.7 pp, 59.6% versus 41.9%.12
  • Speed does not favor Claude here: in AA’s reference GLM-5.3 outputs 98.2 versus 96.8 tokens/s and reaches its first answer token after a 22.7 s median versus 676.8 s.12

Methodology and sources

AA uses the named reasoning configurations; Claude Opus 5.5 is Max with default fallback.123

Coding and reasoning rows use the same AA evaluation versions: Terminal-Bench 4.0, HLE and SciCode. The available official reports do not provide this exact current model pair under a common setup. AA output speed and time to first answer token are model references, not Novita endpoint guarantees; the latter is AA’s median for its 10,000-input-token prompt and includes reasoning for both models. Prices and context windows are Novita AI’s listed values for GLM-5.3 and Anthropic’s for Claude Opus 5.5; the uncached example uses 10,000 input and 1,000 billed output tokens.

Costs are estimates at listed rates for uncached synchronous requests, not measured invoices; billed output includes reasoning. Tax, retries, discounts and negotiated capacity are excluded. Novita AI publishes this page using public sources, without a private workload benchmark.

  1. Artificial Analysis · GLM-5.3 (Max)
  2. Artificial Analysis · Claude Opus 5.5 (Max, Default Fallback)
  3. Anthropic · Opus 5.5 pricing
  4. Novita AI · GLM-5.3 pricing

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