Multimodal (Vision + Text): The Right Question Isn't "Which Model"
Once you've narrowed a model shortlist on the main TokenRate calculator, the Compare Prices side-by-side view is where you stack them for a decision. Each row shows the provider, the model ID (the one you'd paste into your SDK), per-1M input and output costs, the context window, and the blended quality score. For Multimodal (Vision + Text) 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 Multimodal (Vision + Text) traffic patterns?" The Compare Prices grid is built for that question. This guide walks through three picks for Multimodal (Vision + Text) and shows how to grid them. Pair this with flagship/balanced/fast/reasoning LLM tiers, Arena AI leaderboard Elo scores explained, and how LLM quality scores are calculated.
The Workload Profile of Multimodal (Vision + Text)
Multimodal (Vision + Text) workloads have a few distinguishing characteristics: images as input (with token-equivalent cost), text outputs, quality varies sharply across providers on visual reasoning; cost per image varies 5-10x. That profile tells you which columns of the Compare Prices grid matter most. Per-image cost (often quoted alongside per-token), quality (visual benchmarks), output cost (replies can be long). It also tells you which tier you should be in. For most Multimodal (Vision + Text) 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 Opus 4
For Multimodal (Vision + Text), Claude Opus 4 is the default candidate. Pricing: $15.00 input / $75.00 output per 1M tokens. Context: 200K. Quality score: 85. Tier: flagship. Why it wins: best visual reasoning quality at a price point where high-volume vision workloads are affordable. Add it to the Compare Prices grid and the Value column makes the case visually (5.7 quality per dollar of input cost). Where it loses: on very long vision-text mixed workloads where Gemini's 1M context dominates.
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 Multimodal (Vision + Text)
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 Multimodal (Vision + Text) 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. Both the price denominator (OpenRouter) and the quality numerator (Arena AI + Artificial Analysis) refresh hourly. So the comparison you screenshot Monday morning is still trustworthy at standup Tuesday morning — but you should re-run it before a quarterly model-routing review. Run the comparison live at /tools/compare-prices, then bookmark the URL for next month's price audit.