Model comparison
DeepSeek V4.1 Flash vs GLM 5.3 Flash
DeepSeek against Z.ai, compared on context, price, and verified benchmark results.
Catalog record checked September 26, 2026Individual provider fields may changeEvidence confidence: High — see receipts below
DeepSeek
DeepSeek V4.1 Flash
Balanced · Open weights
Z.ai
GLM 5.3 Flash
Balanced · Open weights
| Specification | DeepSeek V4.1 Flash | GLM 5.3 Flash |
|---|---|---|
| Provider | ||
| Provider | DeepSeek | Z.ai |
| Tier | ||
| Tier | Balanced | Balanced |
| Context window | ||
| Context window | 1M | Winner: 1.05M |
| Max output | ||
| Max output | Winner: 384K | 131K |
| Input / 1M tokens | ||
| Input / 1M tokens | $0.30 | Winner: $0.15 |
| Output / 1M tokens | ||
| Output / 1M tokens | $1.20 | Winner: $0.50 |
| Weights | ||
| Weights | Open | Open |
| Parameters | ||
| Parameters | 552B MoE (causal encoder-decoder; 8B active for input, 16B for output) | 320B total / 18B active (MoE) |
| Reasoning levels | ||
| Reasoning levels | low, high, max | Not verifiedUnverified |
| Modalities | ||
| Modalities | text, image | text, image, video |
| License | ||
| License | MIT | MIT |
| API model id | ||
| API model id | deepseek-flash | glm-5.3-flash |
| Released | ||
| Released | September 10, 2026 | August 26, 2026 |
| Artificial Analysis Intelligence Index [max] (2026-09-26) | ||
| Artificial Analysis Intelligence Index [max] (2026-09-26) | 39.5 | Not verifiedUnverified |
| Artificial Analysis Intelligence Index (2026-09-26) | ||
| Artificial Analysis Intelligence Index (2026-09-26) | Not verifiedUnverified | 41.8 |
| DeepSWE 1.1 [max] (2026-09-10) | ||
| DeepSWE 1.1 [max] (2026-09-10) | 74.2 | Not verifiedUnverified |
| DeepSWE 1.1 (2026-08) | ||
| DeepSWE 1.1 (2026-08) | Not verifiedUnverified | 63.4 |
| Terminal-Bench 2.1 [max] (2026-09-10) | ||
| Terminal-Bench 2.1 [max] (2026-09-10) | 90.6 | Not verifiedUnverified |
| Terminal-Bench 2.1 (2026-08) | ||
| Terminal-Bench 2.1 (2026-08) | Not verifiedUnverified | 84.3 |
| GPQA Diamond [max] (2026-09-10) | ||
| GPQA Diamond [max] (2026-09-10) | 90.9 | Not verifiedUnverified |
| SWE-bench Verified (2026-09-01) | ||
| SWE-bench Verified (2026-09-01) | Not verifiedUnverified | 92 |
| Humanity's Last Exam (2026-08) | ||
| Humanity's Last Exam (2026-08) | Not verifiedUnverified | 55.3 |
Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.
Benchmark receipts
Where each score comes from, and how far it can be compared across models.
- 2026-09-26Artificial Analysis
Composite index blending reasoning, knowledge, and coding evals into one 0–100 score.
Comparable with caveatOnly same-version scores are comparable. v4.3.2 was rebaselined (the top score fell from 66 on v4.1.1 to ~58), so v4.1.1 figures must not be compared with v4.3.2 figures — check measuredAt.
- 2026-09-10DeepSeek V4.1-Flash release notes (vendor)
Agentic software engineering: pass@1 on real repo tasks with reported cost per completed task.
Directly comparable
- 2026-09-01vals.ai SWE-bench Verified leaderboard (independent, mini-swe-agent bash-only); not published by Z.ai
Real GitHub issue resolution: does the model's patch pass the hidden tests.
Comparable with caveatPost-audit vendor claims and pre-audit scores sit on different task trust levels; scaffolding (agent harness, compute budget) also dominates results. Never aggregate across scaffolds.
- 2026-08Z.ai GLM-5.3-Flash blog (vendor, mini-swe-agent, 400K context)
Agentic software engineering: pass@1 on real repo tasks with reported cost per completed task.
Directly comparable
- 2026-08GLM-5.3-Flash HF model card (vendor, with tools, full set)
Agentic terminal work: multi-step tasks executed in a sandboxed shell environment.
Comparable with caveatNot comparable with Terminal-Bench 3.0 or 4.0 (different task sets) or v1; harness configuration (container, time limits) also shifts results.
Pricing tiers
DeepSeek V4.1 Flash: Official peak rates $0.30/$1.20 per MTok; off-peak $0.15/$0.60 (50% of peak). Cache hit $0.006 peak / $0.003 off-peak. Peak hours are weekdays 01:00–04:00 and 06:00–10:00 UTC. Open weights (MIT). The legacy deepseek-v4-flash and deepseek-v4-flash-vision-exp ids temporarily route here.
GLM 5.3 Flash: $0.15/$0.50 per MTok on Z.ai's first-party API (cached input $0.03); third-party hosts (GMI, Novita, Together) list the same $0.15/$0.50. No promo tier is listed. The faster GLM-5.3-FlashX costs $0.37/$1.25.
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash is DeepSeek's new default — an open-weight (MIT) multimodal MoE that DeepSeek says beats V4-Pro on performance, cost and speed.
Best for
- Open-weight price-performance
- High-volume hosted agents
- Self-hosting with MIT weights
Watch out
Vendor benchmark claims are far ahead of its independent Artificial Analysis score (39.5 on v4.3.2); peak/off-peak billing means the hour you run matters.
GLM 5.3 Flash
GLM 5.3 Flash is Z.ai's natively multimodal open-weight workhorse — 1M context, hybrid sparse/linear attention, near-flagship Intelligence Index at a fraction of the cost.
Best for
- Cost-efficient long-context
- Multimodal input
- Coding agents
Watch out
Self-hosting needs ~186GB GPU memory at 4-bit (multi-GPU). Third-party hosts charge more than Z.ai's own API.
When the cheaper one wins
GLM 5.3 Flash is cheaper on output at $0.50 per million tokens against $1.20 for DeepSeek V4.1 Flash — about 2.4×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. These are standard-tier API rates, excluding batch and cache discounts.
Run the model pickerEvidence confidence: High
How strong and complete the evidence behind this comparison is — not a prediction of which model is better.
- Pricing verified on both sides — Input and output rates are verified for both models.
- 4/5 core specs verified on both sides — Not published for at least one side: reasoning levels.
- 3 shared named benchmarks with differing scores — Measured on: Artificial Analysis Intelligence Index, DeepSWE 1.1, Terminal-Bench 2.1.
- Verified within the last 90 days — Newest catalog check was 2 days ago.
- Both models carry source citations — Each side has at least two catalog sources on record.
Source receipts
Each catalog figure was checked against the provider or an independent second source on the date shown.
- DeepSeek V4.1 Flash: DeepSeek — V4.1-Flash release (2026-09-10) (accessed 2026-09-26)
- DeepSeek V4.1 Flash: DeepSeek API pricing (deepseek-flash $0.30/$1.20 peak) (accessed 2026-09-26)
- DeepSeek V4.1 Flash: Hugging Face — deepseek-ai/DeepSeek-V4.1-Flash (accessed 2026-09-26)
- GLM 5.3 Flash: Z.ai — GLM-5.3-Flash announcement (accessed 2026-08-29)
- GLM 5.3 Flash: getdeploying — GLM-5.3-Flash reference (accessed 2026-08-29)
- GLM 5.3 Flash: GMI Cloud — GLM-5.3-Flash analysis (accessed 2026-08-29)
- GLM 5.3 Flash: Z.ai pricing (glm-5.3-flash $0.15/$0.50, cached $0.03) (accessed 2026-09-26)
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Diving deeper on one model? DeepSeek V4.1 Flash · GLM 5.3 Flash
Common questions
DeepSeek V4.1 Flash vs GLM 5.3 Flash
Answered from the verified figures on this page rather than general guidance.
Is DeepSeek V4.1 Flash or GLM 5.3 Flash cheaper for input?
Is DeepSeek V4.1 Flash or GLM 5.3 Flash cheaper for output?
Which has the larger context window, DeepSeek V4.1 Flash or GLM 5.3 Flash?
Should I use DeepSeek V4.1 Flash or GLM 5.3 Flash?
Next step
Choosing between them
The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.
- Input price: GLM 5.3 Flash costs $0.15 per 1M tokens versus $0.30 for DeepSeek V4.1 Flash — a 2x difference at the headline tier.
- Context: GLM 5.3 Flash takes 1.05M against 1M for DeepSeek V4.1 Flash — only decisive if your prompts approach the smaller window.
- Measured capability: GLM 5.3 Flash leads Artificial Analysis Intelligence Index 41.8 to 39.5 (measured 2026-09-26).
Choosing a harness rather than a model — Cursor, Copilot, Claude Code, Windsurf, or Lovable? Compare agentic harnesses · Latest releases.