News & guides
DeepSeek V4 Flash vs Qwen 3.6: Rent Cheap or Own It
A 13B-active MoE at fourteen cents per million tokens against a dense 27B you can run yourself. The crossover point is lower than most teams assume.
· 9 min read
DeepSeek V4 Flash: 1M Context at 13B Active Parameters
The cheap half of the DeepSeek V4 family keeps the million-token window and drops active parameters to 13B. What that trade buys, and where it stops working.
· 8 min read
DeepSeek V4 Pro vs Qwen 3.6: 1.6T or 27B, for 3 Points
V4 Pro reports 80.6 percent on SWE-bench Verified, Qwen 3.6 27B reports 77.2. One is sixty times larger. What that tells you about model size in 2026.
· 9 min read
DeepSeek V4 Pro vs V4 Flash: Same Window, Different Brain
Both ship 1M context and an MIT licence. Pro activates 49B parameters per token, Flash 13B. Where that single difference decides which one you should run.
· 8 min readDense vs Mixture-of-Experts: Which You Can Actually Run
MoE models are cheap to compute and expensive to hold. Dense models are the reverse. The architecture decides your deployment more than your benchmark scores.
· 9 min readEvaluating Models on Your Own Codebase
Public benchmarks rank a sample that is not your repository. How to choose tasks, source ground truth and size a set that actually predicts your work.
· 9 min read
Every 1M-Context Model Compared: Which Window Is Real
Five open-weight models now advertise 1M tokens. Advertised context and usable context are different things. How to tell which window actually holds up.
· 9 min read
Fallback Model Selection: Choosing Your Second Model Well
A fallback that behaves nothing like your primary turns an outage into a quality incident. How to pick a second model and prove it works.
· 9 min read
GDPval Explained: Benchmarking Real Professional Work
GDPval scores models on deliverables from real occupations, graded by experts. What that measures, why it is not a coding benchmark, and how to read it.
· 9 min read
GLM-5.2 vs DeepSeek V4 Flash: Ten Times the Price
Both are MIT-licensed with 1M context, but one costs ten times the other. Where the expensive model earns the gap, and where the cheap one quietly wins.
· 9 min readGLM-5.2 vs MiniMax M3: Long-Horizon Code or Cheap Multimodal
Both ship 1M context and target coding. GLM-5.2 is built for unattended agent runs; M3 is multimodal and cheaper. Which one your workload actually needs.
· 9 min readGLM-5.2 vs Qwen 3.6: Sparse and Huge, or Dense and Small
Both are permissively licensed and strong at code. One is a 744B mixture-of-experts, the other a dense 27B on a single GPU. The architecture is the decision.
· 8 min readReady to try it?
Every model behind one key. Start free and upgrade when it sticks.