Translation: The Right Question Isn't "Which Model"
The Compare Prices tool is the fastest way to put a shortlist of LLMs in a single grid — input cost, output cost, context window, and quality score in stacked columns you can scan vertically. Provider dropdowns let you mix models across Anthropic, OpenAI, Google, Meta, DeepSeek, Mistral, and xAI without leaving the page. For Translation the picking decision usually defaults to "use the model my last project used" — which is roughly the worst possible heuristic in 2026, because the price-quality frontier has shifted three times in the past year. The right question is "which combination of price tier, context length, and quality score fits Translation traffic patterns?" The Compare Prices grid is built for that question. This guide walks through three picks for Translation and shows how to grid them. See also: filter LLM models by tier, cost, quality, Value column vs tokens-per-dollar, and how to pick an LLM by quality score and cost.
The Workload Profile of Translation
Translation workloads have a few distinguishing characteristics: input-heavy (source text), 1× output (target text), no chain-of-thought needed, but multilingual quality varies widely across models; quality is workload-make-or-break. That profile tells you which columns of the Compare Prices grid matter most. Quality (specifically multilingual benchmark performance), then input cost (translation is input-heavy). It also tells you which tier you should be in. For most Translation traffic, the Balanced tier is the production default — quality high enough to ship to real users at scale, price low enough to make the unit economics work.
Top Pick: Claude Sonnet 4.7
For Translation, Claude Sonnet 4.7 is the default candidate. Pricing: $3.00 input / $15.00 output per 1M tokens. Context: 200K. Quality score: 80. Tier: balanced. Why it wins: consistent multilingual quality across major languages at production-default pricing. Add it to the Compare Prices grid and the Value column makes the case visually (26.7 quality per dollar of input cost). Where it loses: on low-resource languages where Gemini's broader training set sometimes wins.
Runner-Ups and When to Pick Them
**GPT-5** ($1.25 / $10.00, Q82) — pick this when quality is non-negotiable and the bill is a rounding error against the value of correct output. **Gemini 2.5 Pro** ($1.25 / $10.00, Q78) — pick this when you want the production-default balance of quality (78) and price ($1.25 input). All three live in the same Compare Prices view so the comparison is one screen, not three browser tabs. For workload-specific cost modeling, run your token volume through /tools/api-cost-estimator.
Compare-Prices Workflow for Translation
Workflow: (1) open /tools/compare-prices, (2) check the three picks across their provider dropdowns, (3) sort the resulting grid by Value column, (4) shortlist the top 1-2, (5) run an A/B against your real Translation traffic for a week. The shortlisting step is where 90% of the time savings happen — the grid eliminates obvious losers (low quality, wrong context, output-cost surprises) in seconds. Pricing is pulled live from OpenRouter's models endpoint and revalidated every 60 minutes via Next.js's incremental cache, so the grid you see is at most an hour stale. Quality scores blend Arena AI Elo with Artificial Analysis intelligence-index data on the same cadence. Open /tools/compare-prices now, pick your provider dropdowns, and pin the shortlist that matches your workload.