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Qwen3.8-27B scores 61.7 on SWE-bench Pro, 73.0 on Terminal-Bench 2.1 (Terminus) and 90.3 on LiveCodeBench v6. The model card reports all three. It is a dense 27B model with a native context of 262,144 tokens, extensible to roughly 1,000,000 with scaling, and it is a native vision language model that understands images and video. Source: Qwen/Qwen3.8-27B model card
Claude Opus 4.6 Max is listed at 53.4 on SWE-bench Pro in the same comparison row. The two numbers are not produced under identical conditions: the 61.7 was measured on a refined set in which Qwen corrected problematic tasks and re-ran the baselines, while the 53.4 is the officially reported Opus figure on the original benchmark, and the harnesses differ. Qwen3.8-27B leads Opus 4.6 Max on 16 of the 24 benchmarks in the model card and still loses on knowledge reasoning, including Humanity’s Last Exam at 30.8 against 40.0. Sources: Qwen3.8-27B benchmarks, official and independent, every test where the 27B beats Opus 4.6 Max
| Benchmark | Qwen3.8-27B | Qwen3.6-27B |
|---|---|---|
| SWE-bench Pro | 61.7 | 53.5 |
| Terminal-Bench 2.1 | 73.0 | 63.4 |
| LiveCodeBench v6 | 90.3 | 83.9 |
Source: Qwen3.8-27B benchmarks, Qwen/Qwen3.8-27B
Qwen3.6-27B is a dense 27B model: 77.2 on SWE-bench Verified, 53.5 on SWE-bench Pro, 59.3 on Terminal-Bench 2.0 and 83.9 on LiveCodeBench v6. It is Apache 2.0 licensed and beats the far larger Qwen3.5-397B-A17B on those coding benchmarks. Sources: Qwen/Qwen3.6-27B, the-decoder on Qwen3.6-27B
Qwen3.6-35B-A3B is the sparse model of the same generation, 35B total with about 3B active: 73.4 on SWE-bench Verified, 49.5 on SWE-bench Pro and 80.4 on LiveCodeBench v6, measured with an internal agent scaffold of bash and file edit tools. Source: Qwen/Qwen3.6-35B-A3B
GGUF sizes, taken from the published repositories rather than from a rule of thumb.
| Build | Size |
|---|---|
| Qwen3.8-27B Q4_K_M | 17.1 GB |
| Qwen3.8-27B Q4_K_M (lmstudio-community) | 16.8 GB |
| Qwen3.8-27B UD-Q4_K_XL | 17.9 GB |
| Qwen3.8-27B Q5_K_M | 19.8 GB |
| Qwen3.6-27B Q4_K_M | 16.8 GB |
| Qwen3.6-27B UD-Q4_K_XL | 17.6 GB |
| Qwen3.6-27B Q5_K_M | 19.5 GB |
| Qwen3.6-27B Q6_K | 22.5 GB |
| Qwen3.6-35B-A3B UD-Q4_K_M | 22.1 GB |
| Qwen3.6-35B-A3B UD-Q3_K_M | 16.6 GB |
| GLM-4.7-Flash Q4_K_M | 18.3 GB |
| Qwen3-Coder-30B-A3B-Instruct Q4_K_M | 18.6 GB |
Sources: unsloth/Qwen3.6-27B-GGUF, unsloth/Qwen3.6-35B-A3B-GGUF, unsloth/GLM-4.7-Flash-GGUF, unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF, Qwen3.8-27B GGUF sizes, Unsloth: run Qwen3.8 locally
The reason the sparse model of that generation is squeezed is visible in the table: the 35B-A3B’s 4-bit build is 22.1 GB, and the 3-bit build that does fit is 16.6 GB.
Qwen3.8-27B uses a hybrid stack: sixteen repeats of three Gated DeltaNet layers followed by one Gated Attention layer, so only 16 of its 64 layers hold a growing KV cache. The full 262,144 token context costs roughly 17 GB of cache even with that design, which puts 32k at roughly 2 GB and 64k at roughly twice that. At Q4_K_M with 64k context the model plus cache measures 17.11 GB, or 18.04 GB with the vision tower loaded. Sources: Qwen3.8 27B VRAM and KV cache math, Qwen3.8-27B VRAM requirements
gpt-oss-20b has 20.9B total parameters and 3.61B active per token across 24 layers, 32 experts with the top 4 selected. The released checkpoint is 12.8 GiB, and MXFP4 quantization of the MoE weights lets it run within 16 GB of memory. Sources: gpt-oss model card, arXiv 2508.10925, openai/gpt-oss-20b, deployment centric analysis, arXiv 2508.16700
Qwen3-Coder-30B-A3B-Instruct has 30.5B total parameters with 3.3B active per forward pass, 128 experts with 8 active, and a native 262,144 token context extensible to about 1M. It was trained for agentic coding and tool use. Sources: Qwen/Qwen3-Coder-30B-A3B-Instruct, Bedrock model card
GLM-4.7-Flash is a 30B total, roughly 3B active mixture of experts model from Z.ai. It reports 59.2 on SWE-bench Verified and 64.0 on LiveCodeBench v6. In the same comparison, gpt-oss-20b scores 34 and Qwen3-30B-A3B-Thinking scores 22 on SWE-bench Verified. It also reports 79.5 on tau-squared-Bench for interactive tool invocation. Sources: GLM-4.7 overview, Z.AI developer documentation, Unsloth GLM-4.7-Flash, GLM-4.7-Flash benchmarks and setup
| Card | Memory | Bandwidth |
|---|---|---|
| GeForce RTX 3090 | 24 GB GDDR6X, 384 bit at 19.5 Gbps | 936 GB/s |
| GeForce RTX 4090 | 24 GB GDDR6X | 1008 GB/s |
| Radeon RX 7900 XTX | 24 GB GDDR6 | 960 GB/s |
| GeForce RTX 5090 | 32 GB GDDR7, 512 bit at 28 Gbps | 1792 GB/s |
Sources: NVIDIA RTX 3090 and 3090 Ti, RTX 4090 against RX 7900 XTX, TechSpot, NVIDIA RTX 5090, RTX 5090 Founders Edition review, TechPowerUp
Local token generation is dominated by moving weights through memory, which is why the three 24GB cards sit within about 8 per cent of each other on bandwidth despite two generations between the oldest and the newest.
SWE-bench takes real issues from real repositories and checks whether the model’s final patch makes the repository’s own tests pass, rather than asking for a single function. The score therefore depends on the whole agent around the model as well as the model: the tool set, the prompt, the context length and the number of attempts. Vendor tables are usually run at full precision with a large context and a tuned agent loop, which is a different test from a 4-bit quant at 32k tokens inside a different application.