Three frontier APIs shipped or re-priced within a week of each other, and they sit in three different price bands. Google released Gemini 3.8 Flash on September 2, 2026 at $0.75 per million input tokens and $3.75 per million output tokens. Anthropic released Claude Fable 5.1 the day before at $10 and $50. OpenAI’s GPT-5.6 Sol sits between them at $5 and $30. On Artificial Analysis’ independent Intelligence Index, the three score 59, 57, and 59. That is the whole comparison in one sentence: a model priced at about one thirteenth of the top tier is scoring level with it on the one index that tests all three the same way.
The catch is in the word “index”. Benchmarks count answers per task. Your bill counts tokens per task, and Gemini 3.8 Flash is built to spend more of them. This guide puts the three side by side on specs, on Google’s own benchmark tables (where Fable 5.1 is missing, and we explain why), on Artificial Analysis’ independent numbers, and on the price arithmetic. Then it gives a plain rule for which API fits which workload. The Gemini 3.8 Flash pillar covers the model on its own terms, and Google’s launch post is the primary source for every Google claim below.
One note on timing. Our Gemini 3.7 Flash vs Claude vs GPT comparison from August compared against Claude Fable 5, not 5.1. Anthropic replaced that model on September 1, so this is a fresh pairing, not a refresh.
Specs at a glance
| Gemini 3.8 Flash | Claude Fable 5.1 | GPT-5.6 Sol | |
|---|---|---|---|
| Vendor | Anthropic | OpenAI | |
| Context window | 1,048,576 tokens | 1M tokens | 1M tokens |
| Max output | 65,536 tokens | 128K tokens | 128K tokens |
| Input price (per 1M) | $0.75 intro, $1.50 from Jan 1, 2027 | $10 | $5 |
| Output price (per 1M) | $3.75 intro, $7.50 from Jan 1, 2027 | $50 | $30 |
| Cache read (per 1M) | $0.075 intro, $0.15 from Jan 1 | $0.25 | $0.50 |
| Knowledge cutoff | March 2026 | June 2026 | Not listed here |
| Reasoning control | thinking_level low / medium / high (medium is the default) |
Extended thinking, always on | Effort levels up to xhigh |
| Artificial Analysis index | 59 (high) | 57 (medium) | 59 (xhigh) |
Two things stand out before any benchmark. Gemini 3.8 Flash has half the maximum output of the other two, 65,536 tokens against 128K, which matters for single-shot long documents more than for agent loops that emit short steps. And its intro price expires on December 31, 2026: from January 1 both Gemini rates double, which changes every ratio in this article. The Gemini 3.8 Flash pricing guide walks through the doubling, caching, batch, and grounding rates in full.
The independent number: 59, 57, 59
Artificial Analysis runs the same nine evaluations against every model and publishes one Intelligence Index score. Gemini 3.8 Flash at the high thinking level scores 59, up from 56 for 3.7 Flash and 52 for 3.6 Flash. GPT-5.6 Sol at xhigh effort also scores 59. Claude Fable 5.1 at medium effort scores 57, tied with GPT-5.6 Terra at max and Muse Spark 1.2 at xhigh. Grok 4.6 at medium is the other model at 59.
Read the reasoning-level labels before reading the numbers. Fable 5.1 was tested at medium, not its top setting, and Sol at xhigh, its top setting. A two-point gap across different effort levels is not a ranking, and Artificial Analysis lists it that way for a reason. The defensible statement is narrower: at the settings tested, Gemini 3.8 Flash matches the two premium models on this index, and it’s the cheapest model at 59 by a wide margin.
The same write-up measured what that score costs. Gemini 3.8 Flash at high used 48,000 output tokens per task on average, 30 percent more than 3.7 Flash, and $0.58 per task in API spend. Time per task was 2.5 minutes, output speed about 300 tokens per second, and time to first token 13.3 seconds on the high-reasoning run because the model thinks before it writes. On τ³-Banking, the tool-use eval, it scored 45 percent, 12 points above 3.7 Flash. Keep those numbers in mind when the per-token price looks too good.
Google’s benchmark tables, and who is missing
Google’s Flash page publishes three cross-vendor rows in text form. Claude Fable 5.1 is not in them. Google’s tables carry Anthropic’s Opus 5 and Sonnet 5 instead, and Fable 5.1 had been public for one day when the tables went up. So the Anthropic column below is Opus 5 ($5 / $25) and Sonnet 5 ($2 / $10), not the $10 / $50 model this article is about. Treat it as the best available proxy, not a direct match.
| Benchmark (Google-run) | Gemini 3.8 Flash | Claude Opus 5 | Claude Sonnet 5 | GPT-5.6 Sol | GPT-5.6 Terra |
|---|---|---|---|---|---|
| HLE-Verified | 54.9% | 54.4% | 31.0% | 54.5% | 51.1% |
| Vals Finance Agent v2 | 61.4% | 58.6% | 53.9% | 53.8% | 54.4% |
| Harvey Legal Agent Benchmark | 10.0% | 6.7% | 5.0% | 2.5% | 0.8% |
HLE-Verified is a three-way tie: 54.9, 54.4, and 54.5 sit inside half a point of each other, with Sonnet 5 well behind. Vals Finance Agent v2 is where 3.8 Flash separates, 2.8 points over Opus 5 and 7.6 over Sol on a multi-step agentic finance task. Harvey Legal is the odd one: every model scores under 11 percent, so the ordering says more than the gaps do.
What Google did not publish in text form matters as much. There is no SWE-Bench Pro, Terminal-bench, or OSWorld figure for 3.8 Flash, and the DeepSWE v1.1 result appears only as a claim that the model “outperforms most larger frontier models” at “a fraction of the cost”, with the number in an image table, not in text. For coding and computer-use comparisons you have to go to each vendor’s own runs, which don’t overlap. Anthropic’s are in the Claude Fable 5.1 benchmarks breakdown; OpenAI’s are in the GPT-5.6 Sol benchmarks. Neither vendor ran the other’s model, so Artificial Analysis remains the only like-for-like source.
The price gap, line by line
At intro rates, the arithmetic is blunt. Fable 5.1’s $10 input is 13.3x Gemini 3.8 Flash’s $0.75, and its $50 output is 13.3x the $3.75. GPT-5.6 Sol’s $5 input is 6.7x, and its $30 output is 8x. Cache reads narrow the spread: $0.075 on 3.8 Flash, $0.25 on Fable 5.1 (3.3x), and $0.50 on Sol (6.7x). Fable 5.1’s cache read is half of Sol’s, which is the one line where the most expensive model isn’t the most expensive.
A worked example makes it concrete. Take a run that consumes 1 million input tokens and 200,000 output tokens, all uncached.
- Gemini 3.8 Flash: $0.75 + $0.75 = $1.50
- GPT-5.6 Sol: $5.00 + $6.00 = $11.00
- Claude Fable 5.1: $10.00 + $10.00 = $20.00
From January 1, 2027 the same Gemini run costs $3.00, and the gaps compress to 3.7x for Sol and 6.7x for Fable 5.1. The doubling also applies to 3.6 Flash and 3.7 Flash, so there’s no cheaper Gemini Flash to retreat to. Anthropic’s pricing page lists the Fable 5.1 rates, and our Claude Fable 5.1 pricing guide and GPT-5.6 pricing guide cover the batch and cache tiers for each.
Cost per task, not per token
Here is the caveat that undoes the simple version of that math. Google says Gemini 3.8 Flash “can use more tokens on longer running and complex tasks, by design”. On hard problems it takes smaller reasoning steps, verifies its work, and calls tools iteratively. Artificial Analysis measured the effect: 48,000 output tokens per task at high, up 30 percent from 3.7 Flash, so the cost to run its index rose from $0.40 per task on 3.7 Flash to $0.58 on 3.8 Flash at unchanged per-token prices. At medium it’s $0.41 per task and at low $0.24.
Two consequences follow. First, a 13.3x per-token gap is not a 13.3x bill gap if the premium model finishes in fewer tokens or fewer tool turns. Artificial Analysis hasn’t published a like-for-like per-task figure for Fable 5.1 or Sol in the text we have, so measure it on your own prompts before trusting either direction of the multiplier. Second, on Gemini the thinking level is the cost lever. Dropping a route from high to low cut per-task cost by more than half on Artificial Analysis’ set, and the thinking levels guide shows how to set it per endpoint. The 3.8 Flash vs 3.7 Flash comparison covers the case where the older model, at 31 percent less per task, is still the better buy.
When to pick each
Pick Gemini 3.8 Flash for volume and agents at cost
If you run thousands of agent tasks a day, 3.8 Flash is the default. It matches the premium models on the independent index, leads Google’s finance and legal agent rows, and costs a fraction per token even after the January doubling. It also takes video, audio, and PDF inputs natively, offers Batch at 50 percent off, includes 5,000 free Google Search grounding requests a month, and has a free tier for prototyping. The trade-offs: text-only output, no Live API, a 65,536-token output ceiling, and a token appetite you should budget for at medium or high.
Pick Claude Fable 5.1 for the hardest long-horizon reasoning
Fable 5.1 is the model to escalate to, not start on. Its case is the work where a wrong answer costs more than the tokens: multi-hour research agents, long terminal sessions, reasoning chains where a cheaper model’s evals fall short. Its 128K output and $0.25 cache reads suit prefix-heavy agent loops that reuse the same large context every turn; on those, the cache line makes the effective multiplier much smaller than 13.3x. See what Claude Fable 5.1 is for the model’s own spec sheet.
Pick GPT-5.6 Sol for OpenAI-ecosystem agent runs
Sol scores 59 at xhigh, ties 3.8 Flash on HLE-Verified, and ships with 128K output at $5 / $30. If your agents, evals, and tooling already live in OpenAI’s stack, Sol is the premium tier that doesn’t require a second vendor. It has the most expensive cache reads of the three, so it fits fresh-context runs better than long cached sessions. The Grok 4.6 vs GPT-5.6 vs Claude Fable 5 comparison shows how Sol held up against the previous Anthropic flagship.
Test all three in one Apidog workspace
Every argument above ends the same way: measure it on your own prompts. Apidog is an API client and testing platform, so it doesn’t run any of these models, but it’s a fast way to send the same request to all three providers and compare what comes back.
The setup is one project with three environments. Each environment holds a base URL and a key variable: https://generativelanguage.googleapis.com with GEMINI_API_KEY, Anthropic’s base with your Claude key, and OpenAI’s base with your OpenAI key. Keys stay in environment variables, never in the request body or the shared collection.
Then build one test scenario with three request steps that carry the same prompt. The Gemini step posts to /v1beta/interactions with "model": "gemini-3.8-flash" and "thinking_level": "medium"; the other two post to their provider’s chat or messages endpoint. On each step, add assertions that matter for a comparison: HTTP 200, a JSON path that confirms the answer is present, and a ceiling on the token-usage field so a run that suddenly spends twice as many thinking tokens fails loudly. On Gemini that field is usageMetadata.thoughtsTokenCount for the legacy endpoint; assert on the equivalent usage block for the other two.
Run the scenario against a set of golden prompts, then schedule it so it runs daily. When a vendor changes defaults or a model starts spending more tokens, the failed assertion tells you before the invoice does. The AI agent API testing guide covers the multi-turn version of this pattern, and scheduling API tests in Apidog covers the recurring run. Download Apidog to set up the three environments.
FAQ
Is Gemini 3.8 Flash better than Claude Fable 5.1?
On Artificial Analysis’ index, 3.8 Flash at high scores 59 and Fable 5.1 at medium scores 57, so they’re close at the settings tested. Google’s tables don’t include Fable 5.1, and Anthropic’s benchmarks don’t include 3.8 Flash, so there’s no wider head-to-head. Per token, Flash is 13.3x cheaper at intro rates.
Which of the three is cheapest for agent workloads?
Gemini 3.8 Flash by a wide margin per token, at $0.75 / $3.75 through December 31, 2026. Per task, Artificial Analysis measured $0.58 at high, $0.41 at medium, and $0.24 at low, because the model spends about 30 percent more output tokens than 3.7 Flash. Measure cost per completed task, not per token, before committing.
Do all three have a 1M context window?
Yes. Gemini 3.8 Flash accepts 1,048,576 input tokens, and Fable 5.1 and GPT-5.6 Sol both list 1M. Max output differs: 65,536 tokens on 3.8 Flash against 128K on the other two.
Why isn’t Claude Fable 5.1 in Google’s benchmark tables?
Google’s published rows list Claude Opus 5 and Claude Sonnet 5 as the Anthropic entries. Fable 5.1 shipped on September 1, one day before 3.8 Flash, so it wasn’t included. For Fable 5.1’s own Anthropic-run numbers, see the Claude Fable 5.1 vs Opus 5 comparison.
Does the Gemini price advantage survive 2027?
Partly. From January 1, 2027 Gemini 3.8 Flash moves to $1.50 / $7.50, which makes Fable 5.1 6.7x on both lines and Sol 3.3x on input and 4x on output. Still cheaper, but half the gap.
Which one to call first
Start with Gemini 3.8 Flash for anything you run at volume, set thinking_level per route, and watch tokens per task, not the sticker price. Escalate to Claude Fable 5.1 where your evals on cheaper models fall short and the context is cached across turns. Use GPT-5.6 Sol when the rest of your stack is OpenAI and you want a premium tier without a second vendor. Whichever you pick, put the same prompt through all three in one test scenario first; the price ratio is public, but the cost-per-task ratio on your prompts is yours to measure.
