Blog - ModelHub

"Best coding model" depends on the shape of your workload. A product that writes small snippets in an IDE has different needs from a backend that generates full files, and both differ from an agent that runs tool calls and iterates. Start by defining the constraints you cannot violate: budget per request, max latency, and minimum correctness.

Rule of thumb: pick one "primary" model for correctness, one "fast/cheap" model for drafts, and one "fallback" model for edge cases.

When evaluating candidates, test these categories instead of relying on a single benchmark score:

Instruction following: does it respect formatting, file boundaries, and constraints?
Bug fixing: can it localize issues and propose minimal, safe changes?
Refactoring: does it preserve behavior while improving structure?
Tool use: if you call search/build/test tools, does it converge or loop?

Use the Playground to run the same prompt across multiple models, then validate outputs with real checks (compile/tests/lint). Compare results in Compare once you've found two close candidates.

How to cut AI API cost without sacrificing product quality

Updated: 2026Routing, caching, tiering

Most products overspend because every request goes to the most expensive model. In practice, you can route requests based on risk: low-risk drafts and simple classification go to cheaper models, while high-risk code changes and final answers go to stronger models.

Model tiering: cheap model for draft → strong model for final.
Prompt shaping: shorter prompts reduce tokens and latency; keep only what matters.
Response caching: cache deterministic or highly repetitive prompts and tool results.
Budget caps: set per-request max tokens and enforce fallbacks when exceeded.

Use Pricing to estimate costs for your real token profile, then sanity-check with your expected traffic volume.

Chinese language tasks: selection criteria that actually matter

Updated: 2026Translation, RAG, customer support

For Chinese tasks, "benchmark score" is often less predictive than handling of punctuation, tone, idioms, and mixed-language inputs. You should test your specific domain vocabulary and formatting rules (e.g., legal, medical, game localization, support tickets).

Mixed language: Chinese + English technical terms in the same message.
Terminology consistency: does it keep glossary terms stable?
RAG faithfulness: does it quote provided sources instead of hallucinating?
Politeness & tone: does it match your brand voice?

Start with a small evaluation set (20–50 real examples). Run them in Playground, and track failures as categories you can fix with better prompts or a stronger model.

How to compare models fairly

Updated: 2026Methodology

Comparisons are only meaningful when you hold variables constant. If one model has a longer prompt, a different temperature, or a higher max tokens setting, the outcome can be misleading. Define your evaluation settings once and apply them to every model.

Same prompt, same formatting, same examples.
Same decoding: temperature/top_p/max tokens.
Same tools: if tools are allowed, allow them for all models.
Same scoring: decide success/failure criteria in advance.

Use Compare for structured, side-by-side views, and use the Status page to factor in real-world reliability.

A simple evaluation workflow you can run in 10 minutes

Updated: 2026Fast, repeatable testing

Pick one representative prompt, then vary the constraints (short answer vs detailed, strict format vs freeform). Run 3–5 models in parallel, and record: response quality, latency, and token usage. You'll usually find two clear tiers quickly.

Tip: include one "hard case" prompt that your product frequently fails on. It reveals model differences faster than easy prompts.

Once you have a shortlist, validate with a second batch of real prompts, then update your routing strategy: cheap model for default, strong model for hard cases, and a fallback for availability issues.