Gemini 3.8 Flash is the best AI for transcripts because it can work from the recording itself — audio or video — as well as the text, holds a million tokens, and is cheap enough for every call. That covers most transcript work: cleanup, summaries, quotes and action items. When the job is synthesis across many interviews — finding patterns in a research study — Claude Opus 5.5 reasons more carefully, and Claude Sonnet 5.5 writes the findings up best.
The safest transcripts default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
Gemini 3.8 Flash is the strongest answer here for transcripts — pick it when quality of output matters more than the $0.75/1M/1M input you pay for it.
Claude Sonnet 5.5 is the cheaper way in for transcripts, at $2.00/1M/1M input against Gemini 3.8 Flash's $0.75/1M/1M.
Claude Opus 5.5 is the fastest of these for transcripts — worth it when latency is what the reader notices, not the last few points of reasoning depth.
Gemini 3.8 Flash is the best transcript model for everyday work, and it accepts audio and video.
Claude Opus 5.5 is the better pick for synthesis across many long interviews.
Claude Sonnet 5.5 is the best choice for turning transcript findings into a report.
Use Gemini 3.8 Flash for cleanup, summaries, quotes and action items.
Use Claude Opus 5.5 when insights depend on comparing many interviews or calls.
Use Claude Sonnet 5.5 when the findings need a clear, well-structured write-up.
Switch the scoring lens to see whether the transcripts answer changes when cost, speed, or long-document depth leads the decision.
Anthropic / Balanced / Oct 10, 2026
Near-Opus 5.5 quality on scoped work at half the price.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
The work is long, ambiguous and multi-step — Anthropic positions Opus 5.5 for exactly that — or you need the lowest possible price, where Haiku 5.5 costs a twentieth as much.
Where the transcripts recommendation shifts once you weigh price or latency differently.
Google's fast agentic workhorse — strong coding at Flash pricing.
Anthropic's new coding default — top SWE-bench Pro score at a lower price than Opus 5.
Near-Opus 5.5 quality on scoped work at half the price.
List prices and published scores — the numbers this page's pick is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| Gemini 3.8 FlashGoogle | $0.75/1M | $3.75/1M | $15 | 1M tokens | Very fast | 92 | 86 | 89 |
| Claude Opus 5.5Anthropic | $4.00/1M | $20.00/1M | $80 | 1M tokens | Deliberate | 100 | 97 | 99 |
| Claude Sonnet 5.5Anthropic | $2.00/1M | $10.00/1M | $40 | 1M tokens | Fast | 98 | 98 | 95 |
Scores out of 100 — how we evaluate models. “Est. month” is 10M in / 2M out at list price: a ceiling, no discounts.
Why each one is on the shortlist for transcripts, what it is genuinely good at, and where we would steer you away from it.
Our pick for transcripts. It scores 92/100 on the coding axis we weight this page by, and nothing else in this shortlist matches it on output quality.
Google's September 2, 2026 Flash model, positioned as the agentic workhorse of the Gemini 3 family — stronger coding and terminal work than 3.7 Flash at the same $0.75/$3.75 price.
You need long single responses (65K output cap) or your workload is output-heavy enough that its verbosity erases the per-token saving.
The fastest capable model in its price range. Gemini 3.8 Flash is the pick for multimodal and terminal-heavy agent work on a budget; just measure cost per task, not per token, because it writes a lot.
Full pricing, benchmark table and release notes on the Gemini 3.8 Flash page.
Here for latency: it answers fastest of anything listed for transcripts, at 100/100 on coding.
Anthropic's September 22, 2026 Opus — a step up from Opus 5 on agentic coding, long-running agent work and vision, at a lower $4/$20 list price and with fewer tokens spent per finished task.
Your tasks are well-scoped and latency-sensitive — Sonnet 5.5 is half the price and Anthropic says it is the fastest Sonnet yet.
The new premium coding default from Anthropic. Opus 5.5 posts the highest SWE-bench Pro figure in any launch table we track and costs less than Opus 5 did. Fable 5.1 still makes sense for the hardest open-ended research at $10/$50; for almost everything else Opus 5.5 is the better buy, and Sonnet 5.5 at half the price is close behind on well-scoped work.
Full pricing, benchmark table and release notes on the Claude Opus 5.5 page.
The value option for transcripts: balanced-tier pricing, at 98/100 on coding. Worth starting here and moving up only if the output disappoints.
Near-Opus 5.5 quality on scoped work at half the price. Full Claude Sonnet 5.5 review →
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
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For transcripts, Gemini 3.8 Flash (Google) is our pick. Google's fast agentic workhorse — strong coding at Flash pricing. It costs $0.75/1M input and $3.75/1M output tokens, with a 1M-token context window — enough headroom for all but the largest transcripts jobs. Claude Opus 5.5 is the closest alternative if it doesn't fit your setup.
Because the work it is built for overlaps closely with transcripts: terminal and coding agents — Google reports 90.8% on Terminal-Bench 2.1, up from 81.6% for 3.7 Flash and video, image and PDF understanding in one 1M-context call. The fastest capable model in its price range. Gemini 3.8 Flash is the pick for multimodal and terminal-heavy agent work on a budget; just measure cost per task, not per token, because it writes a lot.
On a moderate month — 10M input and 2M output tokens — Gemini 3.8 Flash runs about $15.00 at list price, with no batch or caching discounts applied, so treat that as a ceiling. It is also the cheapest option among the models compared here, so for transcripts there is no cost argument for dropping to something weaker.
Verbose: Artificial Analysis needed 120M output tokens to run its index against a 71M median, so cost per task runs above the sticker price. 65K max output — half of what Claude and OpenAI's current models allow. Avoid it if you need long single responses (65K output cap) or your workload is output-heavy enough that its verbosity erases the per-token saving. None of that rules it out for transcripts on its own — but if one of those limits maps onto how you actually work, take the alternative on this page seriously rather than defaulting to the top pick.