--- title: "GLM 5.3 vs DeepSeek V4.1 Flash: Benchmarks, Price, and Hardware (2026)" slug: glm-5-3-vs-deepseek-v4-1-flash-2026 description: "Z.ai's 753B flagship against DeepSeek's 552B Flash: the Flash wins the two coding benchmarks both vendors publish, the flagship wins the independent composite, and the price gap is about five to one. Which one earns it." author: "Yotta Labs" date: 2026-09-24 categories: ["Inference"] canonical: https://www.yottalabs.ai/post/glm-5-3-vs-deepseek-v4-1-flash-2026 --- # GLM 5.3 vs DeepSeek V4.1 Flash: Benchmarks, Price, and Hardware (2026) ![](https://cdn.sanity.io/images/wy75wyma/production/7be67b7bfb64e9c0e3943dc48bbc4ea8c1483368-1200x627.png) *The flagship costs five times more than the Flash. On the benchmarks both vendors publish, the Flash scores higher. Here’s what that does and doesn’t mean.* This is the comparison people started typing the day DeepSeek V4.1 Flash launched: not Flash against Flash, but Z.ai’s full-size GLM 5.3 against a DeepSeek model that costs a fraction of it and, by DeepSeek’s own table, beats DeepSeek’s own flagship. The question is whether a 753-billion-parameter model with a $4.40 output rate still earns its place next to a 552-billion-parameter one at $1.20. The [Flash-versus-Flash comparison](https://www.yottalabs.ai/post/deepseek-v4-1-flash-vs-glm-5-3-flash-2026) is a different post; this one is about the top of Z.ai’s line. The short version: on the two agentic coding benchmarks that appear in both vendors’ launch tables, V4.1 Flash is ahead. On the one independent composite score that covers both, GLM 5.3 is ahead. And on price, hardware, and license, the two models are far enough apart that most teams will pick on those before they pick on benchmarks. ## The two models side by side ## Benchmarks: two rulers, two different winners Both vendors published launch tables, and for once they overlap on more than one test. Terminal-Bench 2.1 and DeepSWE v1.1 appear in both, and both tables also include the same reference scores for DeepSeek V4 Pro (87.9 and 62.7) and Kimi K3 (88.3 and 67.5), which is a small sign the two labs are at least measuring against the same numbers. On those two tests, V4.1 Flash leads: 90.6 to 88.2 on Terminal-Bench 2.1, 74.2 to 66.9 on DeepSWE. Both figures are each vendor’s own run at maximum reasoning effort, not a controlled head-to-head, so the 2.4-point Terminal-Bench gap is inside what settings can move. The 7.3-point DeepSWE gap probably isn’t. Read together with the rest of DeepSeek’s table, where V4.1 Flash also passes V4 Pro on both, the consistent story is that DeepSeek’s new architecture is unusually strong on agentic coding for its active-parameter count, and that’s the workload GLM 5.3’s post-training was aimed at too. GLM 5.3’s table goes wider than DeepSeek’s. Z.ai reports 62.5 on Humanity’s Last Exam with tools, 73.0 on Toolathlon Verified, 84.5 on CyberGym, and a six-fold jump over GLM 5.2 on Terminal-Bench 3.0 (28.3 against 4.6). DeepSeek doesn’t report any of those for V4.1 Flash, and its knowledge numbers (GPQA Diamond 90.9 on the model card, 36.8 on Humanity’s Last Exam in its launch materials with no stated tools setting) aren’t comparable to Z.ai’s with-tools figure. So outside the two shared tests, the tables don’t meet. The independent number goes the other way. Artificial Analysis scores GLM 5.3 at 45 on its Intelligence Index (version 4.3.2) against 39 for V4.1 Flash, with GLM 5.3 Flash at 42 in between. Six points is a real gap on that index. It measures a broad mix of reasoning, knowledge, and coding tasks under one methodology, which is exactly what the vendor tables don’t do, and it says the flagship is the more capable general model. Put the two together and you get the honest reading: if your traffic is agentic coding and terminal work, the vendor evidence says the Flash is at least as good and probably better. If your traffic is broad, knowledge-heavy, or long-horizon reasoning, the independent evidence says the flagship is ahead. Neither table has been independently replicated, and the [GLM 5.3 what’s new post](https://www.yottalabs.ai/post/glm-5-3-whats-new-benchmarks-how-to-access-2026) has the full flagship table with the caveats. ## Price: about five to one GLM 5.3 is $1.40 per million input tokens and $4.40 output on Z.ai’s API, flat, no time-of-day windows, and the same on [Yotta AI Gateway](https://www.yottalabs.ai/ai-gateway). DeepSeek V4.1 Flash is $0.30 and $1.20 at peak (weekday Beijing business hours, 01:00 to 04:00 and 06:00 to 10:00 UTC) and half that off-peak. Artificial Analysis’s blended rate, weighted 3:1 input to output, comes out to about $0.90 per million for GLM 5.3 and $0.18 for V4.1 Flash. So the flagship costs roughly four to five times more at DeepSeek’s peak and seven to nine times more off-peak. Caching widens it further: DeepSeek charges $0.003 per million for cache hits off-peak, and an agent loop that resends a long system prompt every turn is mostly cache hits. Z.ai publishes a cached-input rate for GLM 5.3 Flash ($0.03); we haven’t confirmed one for the flagship, so check its rate card before modeling cache-heavy traffic. The [V4.1 Flash launch post](https://www.yottalabs.ai/post/deepseek-v4-1-flash-pricing-specs-v4-pro-routing-2026) has the full DeepSeek price table and peak schedule. The one thing GLM’s price buys that’s not on the benchmark table is predictability. A flat rate at a flat rate is easy to budget; a peak window in someone else’s time zone is a line in a spreadsheet you have to remember. ## Speed: not close Artificial Analysis measures V4.1 Flash at 227 output tokens per second with a 0.98-second time to first token on DeepSeek’s API, against 58.8 tokens per second and 3.37 seconds for GLM 5.3 on Z.ai’s. That’s a four-fold decode gap and a three-second difference before the first token arrives. Part of it is architecture (16B active parameters on decode against about 40B) and part is DSpark, the speculative decoding DeepSeek builds into the model. For an interactive agent loop, this is the largest practical difference between the two, and no benchmark score offsets it. ## Hardware: the flagship needs the bigger node Both are open weights now, and both are node-scale. GLM 5.3’s FP8 checkpoint is about 744 GB, which doesn’t fit eight H100s (640 GB) and needs an 8x H200 node (1,128 GB) or an 8-GPU Blackwell node; the [GLM 5.3 hardware requirements](https://www.yottalabs.ai/post/glm-5-3-hardware-requirements-gpu-memory-2026) post has the configs, the INT4 route down to 4x H200, and the vLLM launch command. V4.1 Flash is about 510 GB, which fits 8x H100 with roughly 130 GB left for KV cache and batching, and runs comfortably on 8x H200; the [V4.1 Flash hardware requirements](https://www.yottalabs.ai/post/deepseek-v4-1-flash-hardware-requirements-gpu-memory-2026) post covers it. The KV cache favors DeepSeek too: its compressed attention brings V4.1 Flash’s cache to 890 bytes per token, so a 1M-token context costs well under a gigabyte. On the same node, that’s the difference between headroom for long contexts and headroom for concurrency, and V4.1 Flash has more of both. If you’re renting, [8-GPU H200 and B300 nodes](https://www.yottalabs.ai/pricing) run either model; the flagship simply has no cheaper option. ## License: MIT against a custom license DeepSeek V4.1 Flash is MIT, like DeepSeek’s other recent releases. GLM 5.3 ships under a custom “glm-5.3” license on Hugging Face, a change from the MIT that GLM 5.2 and GLM 5.3 Flash use. Z.ai hasn’t published a plain-language summary of what it restricts. For evaluation and internal use it’s unlikely to matter; for redistributing weights, fine-tuning and shipping the result, or building a hosted product on them, read the license file before you commit, because it’s the one open-weight model in this comparison where the answer isn’t already known. ## What the flagship still has Three things. Breadth, on the independent index, six points ahead. A wider published record on tool use, security research, and long-horizon tasks, which is where Z.ai spent its post-training budget and where its own table is deepest. And flat, predictable pricing on two APIs, including [Yotta AI Gateway](https://www.yottalabs.ai/ai-gateway), where it sits next to GLM 5.2, DeepSeek V4 Pro and V4 Flash, Qwen 3.8, Kimi K3, and Grok 4.6 behind one OpenAI-compatible interface. That last point is practical. V4.1 Flash isn’t on the Gateway yet, and DeepSeek’s own API comes with the peak-hour clock. GLM 5.3 is one model string away from the rest of the catalog, so an A/B against anything else on the Gateway is routing, not integration. ## Which one to use Choose GLM 5.3 if your workload is broad rather than coding-specific, if you want the higher independent score, if flat pricing without a clock matters for budgeting, or if you want it behind the same key as the rest of your models on the Gateway. Choose DeepSeek V4.1 Flash if your traffic is agentic coding and terminal work (the shared benchmarks favor it), if latency matters (four times the decode speed), if you’re cache-heavy and mostly off-peak (the $0.003 cache-hit rate is the cheapest in this class), if you need image input (the flagship is text-only), or if you’ll self-host and want the model that fits 8x H100 under MIT. If the workload is mixed, the answer most teams land on is both: route the hard, broad, long-horizon tail to the flagship and the coding volume to the Flash, and move the boundary with your own evals. Both speak OpenAI-compatible interfaces, so the split is a config change. The [Best Chinese LLMs roundup](https://www.yottalabs.ai/post/best-chinese-llm-models-2026-deepseek-qwen-glm-kimi-compared) has the wider field if neither fits. ## Frequently asked questions **Is DeepSeek V4.1 Flash better than GLM 5.3?** On the two coding benchmarks both vendors publish, yes: 90.6 vs 88.2 on Terminal-Bench 2.1 and 74.2 vs 66.9 on DeepSWE v1.1, each vendor’s own run. On Artificial Analysis’s independent Intelligence Index, no: GLM 5.3 scores 45 to V4.1 Flash’s 39. Which matters depends on whether your work looks like the coding benchmarks or the composite. **Which is cheaper?** V4.1 Flash, by a lot. $0.30 / $1.20 at peak and $0.15 / $0.60 off-peak against $1.40 / $4.40 for GLM 5.3, roughly four to nine times cheaper depending on the hour and direction, and far cheaper again on cache hits. **Which is faster?** V4.1 Flash: 227 output tokens per second and a 0.98-second time to first token on Artificial Analysis’s measurements, against 58.8 tokens per second and 3.37 seconds for GLM 5.3. **Which needs less hardware?** V4.1 Flash. About 510 GB in FP8, which fits an 8x H100 node. GLM 5.3 is about 744 GB and needs 8x H200 at minimum for the FP8 checkpoint. **Are both open weights?** Yes. V4.1 Flash is MIT. GLM 5.3’s weights are on Hugging Face under a custom “glm-5.3” license, which you should read before commercial use. **Does GLM 5.3 take images?** No, the flagship is text-only. V4.1 Flash takes image input natively. GLM 5.3 Flash takes image and video, if you need vision in the GLM family. **How does GLM 5.3 compare to DeepSeek V4 Pro?** By Z.ai’s table, GLM 5.3 edges V4 Pro on Terminal-Bench 2.1 (88.2 vs 87.9) and DeepSWE (66.9 vs 62.7). V4 Pro remains on DeepSeek’s API at unchanged prices after DeepSeek reversed its plan to retire it, and it’s on Yotta AI Gateway at $0.99 / $2.97; the [V4.1 Pro tracking post](https://www.yottalabs.ai/post/deepseek-v4-1-pro-release-date-what-is-known-how-to-prepare-2026) covers what replaces it. **Is either on Yotta AI Gateway?** GLM 5.3 is live at $1.40 / $4.40, flat. DeepSeek V4.1 Flash isn’t yet; the Gateway carries DeepSeek V4 Pro and V4 Flash. This post will be updated if V4.1 Flash is added. ## Bottom line GLM 5.3 and DeepSeek V4.1 Flash are a flagship and a Flash that overlap more than their price tags suggest. The Flash wins the coding benchmarks both vendors publish, wins on speed by four times, wins on price by four to nine times, fits smaller hardware, and ships under MIT. The flagship wins the independent composite by six points, has the deeper published record on tool use and long-horizon work, and comes with flat pricing and a place on the Gateway next to everything else. If you had to run one, the Flash is the rational default for coding traffic and the flagship for everything broader. Since you don’t have to run one, put GLM 5.3 through [Yotta AI Gateway](https://www.yottalabs.ai/ai-gateway) and V4.1 Flash through DeepSeek’s API on the same eval set this week, and let your own traffic settle which one earns the price difference.