LLM Comparison
GLM-5.3-Flash vs Hunyuan A13B Instruct
Side-by-side specs, pricing & capabilities
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2/6 models| Organization | ||
| OpenTools Score | 74 456 | |
| Family | GLM | Hunyuan |
| Status | Current | Current |
| Release Date | Aug 2026 | Jul 2025 |
| Context Window | 1.3M tokens | 131K tokens |
| Input Price | $0.07/M tokens | $0.14/M tokens |
| Output Price | $0.25/M tokens | $0.57/M tokens |
| Pricing Notes | OpenRouter lists a limited-time discounted price of $0.075/M input and $0.25/M output. Standard first-party style pricing is commonly $0.15/M input and $0.50/M output; cache-read pricing may vary by provider. | — |
| Capabilities | textvisionvideo-inputcodetool-usereasoninglong-contextopen-weights | textcode |
| Training Cutoff | Not disclosed | — |
| Max Output | 131K tokens | 131K tokens |
| API Identifier | z-ai/glm-5.3-flash | tencent/hunyuan-a13b-instruct |
| Benchmarks | ||
| Artificial Analysis Intelligence Index v4.3 | 41.9 indexartificial-analysis | — |
| GPQA Diamond | 91.2%artificial-analysis | — |
| SciCode | 51.6%artificial-analysis | — |
| Terminal-Bench 2.1 | 84.3%artificial-analysis | — |
| View GLM-5.3-Flash | View Hunyuan A13B Instruct | |
Cost Calculator
Enter your expected monthly token usage to compare costs.
| Model | Input | Output | Total / mo | vs Best |
|---|---|---|---|---|
| GLM-5.3-FlashCheapest | $0.08 | $0.13 | $0.20 | — |
| Hunyuan A13B Instruct | $0.14 | $0.29 | $0.43 | +112% |
Compare hosting configurations
Compare two exact setups, including different configurations of the same model. Hardware fit, measured speed and costs are separate decisions.
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Use hours for the same scenario period. Cloud billing includes idle time; active hours alone do not set the bill. Local electricity uses whole-system attributable power, powered hours and your tariff. Missing inputs remain unknown.
Measured speed needs the same pinned workload protocol, tokenizer, context, batching and cache state.
| Configuration | Run full native GLM-5.3-Flash image, video and reasoning on an owned DGX H200 TP4 subset | Run Hunyuan A13B fast-mode text on a 64 GiB Intel desktop |
|---|---|---|
| Model | GLM-5.3-Flash | Hunyuan A13B Instruct |
| Hardware | Owned NVIDIA DGX H200; 4 of 8 GPUs, diagnostic TP4 | Intel Core i5-12400 desktop, 8 inference threads, 64 GiB RAM |
| Runtime | GLM 5.3 Flash full native NVIDIA immutable image Official image 0674a3ea, declared vLLM 385dce36 / Python 3.12 / CUDA 13.0.2; reader source guards required · CUDA | llama.cpp v0.7.0 (620d2225cc436ab0b001e3200e590e3b286ef9ab) · CPU |
| Artifact | GLM-5.3-Flash complete native mixed FP8/BF16 multimodal checkpoint · Native mixed FP8 with the exact BF16 vision, attention, KDA and mHC exclusions retained · Native FP8 language experts with exact BF16 exclusions and full BF16 vision; no re-quantization/component drop | Hunyuan A13B Instruct Q4_K_M publisher GGUF · GGUF Q4_K_M mixed quantization · Q4_K_M GGUF quantization published by Tencent |
| Context / concurrency | 16,384 tokens / 1 | 4,096 tokens / 1 |
| Required memory | Unknown (unknown) | Unknown (unknown) |
| Evidence | Documented setup | Documented setup |
| Hardware fit | Hardware fit unknown Your operating system is unknown. Runtime support has not been confirmed. Full weights, BF16 vision, KDA recurrent/convolution states, sparse MLA, mHC, MTP, collectives, kernels and caches have not been measured. Enter available memory per device or unified memory pool. Host RAM: Preserve the DGX vendor 2 TB and AWS 2,048 GiB source labels. Reader records actual MemTotal; no nominal-to-usable threshold or measured minimum is asserted. Free disk space: A 1 TiB usable working-space guard is prescribed for assets, image/staging and results. It is a conservative reader allocation, not a measured minimum or proof the unpacked runtime fits. Container requirement: Separate immutable Linux amd64 container; host/container versions not conflated | Hardware fit unknown No exact full-process peak for Hunyuan A13B Q4_K_M and CPU workload. Enter available memory per device or unified memory pool. Host RAM: 64 GiB is chosen host capacity, not a measured minimum. Free disk space: 80 GiB is an author free/root disk allocation, not measured disk minimum. |
| One-time cost | Unknown Existing hardware/allocation: Existing owned acquisition cost is unknown. Reader setup labor: Reader download, image pull, setup and labor costs are unknown and separate from recurring use. | Unknown hardware: Existing desktop purchase price and incremental hardware cost depend on the reader; unknown. setup: Initial provisioning/build time and labor cost are unknown. |
| Recurring scenario cost | Unknown Owned electricity: Owned-host electricity/power price and measured use are unknown. Retained local/storage: Existing NVMe retention/opportunity cost is unknown. Instance-store data may be lost on stop or termination; copy wanted results first. Network/egress: Network, data-transfer charges and taxes are unknown; no free assumption. Administration: Reader-specific labor and administration costs are unknown. One-time setup and recurring costs are separate. Unknown components are not treated as free. No monthly token output is inferred from peak throughput. | Unknown electricity: Actual wall power, electricity rate and powered hours are unknown; no TDP-to-wall-power conversion. storage: Incremental cost of local disk capacity is unknown. The 80 GiB free-disk walkthrough allocation is an author choice, not a measured requirement. egress: Data transfer price, allowances and traffic volume are unknown. administration: Maintenance labor rate and hours are unknown. One-time setup and recurring costs are separate. Unknown components are not treated as free. No monthly token output is inferred from peak throughput. |
| Published measurements (unranked) | No compatible measurement available | No compatible measurement available |
Different currencies and incomplete costs are not ranked. Model quality benchmarks remain on the model pages; they do not establish quality or speed for every deployment.
Z.ai
GLM-5.3-Flash
GLM-5.3-Flash is Z.ai’s open-weight GLM-5 model for efficient coding, long-horizon agent tasks, and long-context multimodal reasoning. It uses a hybrid sparse and linear attention design with 320B total parameters and 18B active parameters.
Tencent
Hunyuan A13B Instruct
Hunyuan A13B Instruct is a large language model from Tencent. Supports up to 131,072 token context window. Available from $0.14/M input tokens.
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