Model comparison
GLM vs DeepSeek: GLM-5.3 and DeepSeek V4.1 Flash
Choose DeepSeek V4.1 Flash for the higher reported DeepSWE v1.1 score (74.2% vs 66.9%); DeepSeek V4.1 Flash has the lower fixed-token cost, $0.00420 for 10,000 input and 1,000 billed output tokens.
| Dimension | Winner | Margin | Evidence |
|---|---|---|---|
| Coding benchmark | DeepSeek V4.1 Flash | +7.3 pp reported score gap | DeepSWE v1.1: DeepSeek V4.1 Flash 74.2% vs GLM-5.3 66.9%1 |
| Reasoning benchmark | DeepSeek V4.1 Flash | +2.8 pp reported score gap | GPQA Diamond: DeepSeek V4.1 Flash 90.9% vs GLM-5.3 88.1%1 |
| Estimated request cost | DeepSeek V4.1 Flash | $0.01420 difference per example | DeepSeek V4.1 Flash $0.00420 vs GLM-5.3 $0.0184023 |
Official benchmark results
Official benchmark results
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| Metric | GLM-5.3 | DeepSeek V4.1 Flash |
|---|---|---|
| DeepSWE v1.1 (Resolved)1 | 66.9% · vendor-reported, 2026 | 74.2% · vendor-reported, 2026 |
| GPQA Diamond (Pass@1)1 | 88.1% · vendor-reported, 2026 | 90.9% · vendor-reported, 2026 |
| HLE w/ tools (Pass@1)1 | 62.5% · vendor-reported, 2026 | 63.9% · vendor-reported, 2026 |
| NL2Repo-Bench1 | 58.0 · vendor-reported, 2026 | 64.0 · vendor-reported, 2026 |
| HLE (text-only subset)1 | 42.0% · vendor-reported, 2026 | 39.1% · vendor-reported, 2026 |
Performance
Performance
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Pricing
Pricing
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| Metric | GLM-5.3 | DeepSeek V4.1 Flash |
|---|---|---|
| Input / 1M tokens23 | $1.40 | $0.30 |
| Output / 1M tokens23 | $4.40 | $1.20 |
| Context window (tokens)23 | 1M | 1M |
| Max output (tokens)23 | 128K | 384K |
| API and model ID23 | zai-org/glm-5.3 | deepseek/deepseek-v4.1-flash |
Which to choose
Coding, reasoning, context and cost
GLM-5.3
- GLM-5.3 is the text-only HLE pick: 42.0% versus DeepSeek’s 39.1%; DeepSeek leads GPQA instead.
- Its 1M context matches DeepSeek’s listed input window, but 128K max output is one-third of DeepSeek’s 384K.
- The GLM request estimate is $0.01840 versus $0.00420; it is the more expensive choice at this fixed token budget.
DeepSeek V4.1 Flash
- Prefer DeepSeek for coding agents: DeepSWE v1.1 is 74.2% versus 66.9%, and NL2Repo is 64.0 versus 58.0.
- For long generated artifacts, the 384K output ceiling is larger than GLM’s 128K, while both list 1M context.
- For cost-sensitive requests, DeepSeek’s $0.00420 example saves $0.01420 relative to GLM.
Methodology
Methodology and sources
GLM-5.3 and DeepSeek V4.1 Flash scores are taken from DeepSeek's official maximum-effort comparison, with tool-enabled HLE and HLE without tools kept separate. Novita serves DeepSeek in fp8 with high reasoning by default; the report and AA use Max reference configurations. AA's output rates describe those reference models after streaming begins, not the Novita endpoints.
Novita AI publishes this GLM and DeepSeek comparison from public reports and model pages. The cost example fixes 10,000 input and 1,000 billed output tokens, including reasoning, at the listed uncached synchronous rates. It excludes tax, retries, discounts and negotiated capacity; no private workload was run.
For GLM and DeepSeek, the first-answer illustration adds the time for 2,000 reasoning tokens to AA's first-chunk field. Calculations use unrounded source fields even though the table displays rounded inputs; the result is a fixed-budget estimate, not a prompt response measurement.
- DeepSeek · V4.1 Flash Model Card huggingface.co · Checked 2026-10-15
- GLM-5.3 · Novita AI novita.ai · Checked 2026-10-15
- DeepSeek V4.1 Flash · Novita AI novita.ai · Checked 2026-10-15
- GLM-5.3 · Artificial Analysis artificialanalysis.ai · Checked 2026-10-15
- DeepSeek V4.1 Flash · Artificial Analysis artificialanalysis.ai · Checked 2026-10-15
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