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Posted on Sep 3 Originally published at apidog.com

Gemini 3.8 Flash vs Claude Fable 5.1 vs GPT-5.6 Sol: which API should developers use?

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Gemini 3.8 Flash vs Claude Fable 5.1 vs GPT-5.6 Sol: Which API Should You Use?

Three frontier APIs shipped or changed pricing within a week of one another, landing in three distinct 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, they score 59, 57, and 59 respectively. A model priced at roughly one-thirteenth of the top tier matches it on the only index that evaluates all three the same way.

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The catch is the word “index.” Benchmarks count answers per task; your bill counts tokens per task. Gemini 3.8 Flash is designed to spend more of them.

This guide compares the three models by:

Published specifications
Google’s cross-vendor benchmark tables
Artificial Analysis’ independent results
Input, output, and cache pricing
Cost per completed task
Best-fit workloads

Google’s launch post is the primary source for the Gemini claims below. For context, our August comparison used Claude Fable 5, not 5.1. Anthropic replaced that model on September 1, making this a new comparison rather than a refresh.

Specs at a glance

Specification	Gemini 3.8 Flash	Claude Fable 5.1	GPT-5.6 Sol
Vendor	Google	Anthropic	OpenAI
Context window	1,048,576 tokens	1M tokens	1M tokens
Maximum output	65,536 tokens	128K tokens	128K tokens
Input price per 1M tokens	$0.75 introductory; $1.50 from Jan. 1, 2027	$10	$5
Output price per 1M tokens	$3.75 introductory; $7.50 from Jan. 1, 2027	$50	$30
Cache read per 1M tokens	$0.075 introductory; $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 default	Extended thinking, always on	Effort levels up to `xhigh`
Artificial Analysis Index	59 at high	57 at medium	59 at `xhigh`

Two specifications matter immediately:

Gemini 3.8 Flash has half the maximum output of the other two: 65,536 versus 128K tokens. That matters for single-shot long documents more than for agent loops that emit short steps.
Gemini’s introductory pricing ends on December 31, 2026. Both rates double on January 1, 2027, changing every price ratio in this article.

The Gemini 3.8 Flash pricing guide covers the doubling, caching, batch, and grounding rates in detail.

The independent result: 59, 57, and 59

Artificial Analysis runs the same nine evaluations against each model and publishes one Intelligence Index score:

Gemini 3.8 Flash at high thinking: 59
GPT-5.6 Sol at xhigh effort: 59
Claude Fable 5.1 at medium effort: 57

Gemini 3.8 Flash improved from 56 for 3.7 Flash and 52 for 3.6 Flash. Fable 5.1’s score of 57 also matches GPT-5.6 Terra at max effort and Muse Spark 1.2 at xhigh. Grok 4.6 at medium is another model at 59.

Read the reasoning settings before interpreting those numbers. Fable 5.1 was tested at medium, while Sol was tested at its top listed setting, xhigh. A two-point difference across different effort levels is not a definitive ranking.

The defensible conclusion is narrower: at the tested settings, Gemini 3.8 Flash matches the two premium models on this index and is the cheapest model with a score of 59 by a wide margin.

What that score costs

Artificial Analysis also measured token usage and latency for Gemini 3.8 Flash at high thinking:

Average output per task: 48,000 tokens
Output usage: 30% more than Gemini 3.7 Flash
API spend: $0.58 per task
Time per task: 2.5 minutes
Output speed: about 300 tokens per second
Time to first [REDACTED CREDENTIAL] seconds**
τ³-Banking tool-use score: 45%, 12 points above Gemini 3.7 Flash

The model thinks before it writes, which explains the relatively high time to first token. Keep these measurements in mind when the per-token price appears too attractive.

Google’s benchmark tables—and the missing model

Google’s Flash page publishes three cross-vendor benchmark rows in text form, but Claude Fable 5.1 is not included. The Anthropic entries are Claude Opus 5 and Claude Sonnet 5 instead. Fable 5.1 had been public for only one day when Google’s tables were published.

The Anthropic results below are therefore proxies, not direct results for Fable 5.1:

Google-run benchmark	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 effectively a three-way tie: Gemini 3.8 Flash, Opus 5, and Sol are within half a point of one another.

Vals Finance Agent v2 is where Gemini 3.8 Flash separates:

2.8 points above Opus 5
7.6 points above Sol

Harvey Legal is the outlier. Every model scores below 11%, so the ordering is more meaningful than the gaps.

Google did not publish text results for:

SWE-Bench Pro
Terminal-bench
OSWorld

The DeepSWE v1.1 result appears only as an image-table claim that Gemini 3.8 Flash “outperforms most larger frontier models” at “a fraction of the cost.”

For coding and computer-use comparisons, you must rely on separate vendor-run evaluations, which do not overlap. Anthropic’s results are in the Claude Fable 5.1 benchmarks breakdown, and OpenAI’s are in the GPT-5.6 Sol benchmarks. Artificial Analysis remains the only like-for-like source in this comparison.

The price gap, line by line

At introductory rates:

Fable 5.1 input is 13.3× Gemini 3.8 Flash input
Fable 5.1 output is 13.3× Gemini 3.8 Flash output
Sol input is 6.7× Gemini input
Sol output is 8× Gemini output

Cache reads narrow the difference:

Gemini 3.8 Flash: $0.075 per 1M tokens
Fable 5.1: $0.25 per 1M tokens, or 3.3× Gemini
Sol: $0.50 per 1M tokens, or 6.7× Gemini

Fable 5.1’s cache-read price is half of Sol’s—the one pricing line where the most expensive model is not the most expensive option.

Worked example

Assume a request uses 1 million input tokens and 200,000 output tokens, with no cached input:

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 request costs $3.00. The gaps then compress to:

Sol: 3.7× Gemini’s cost
Fable 5.1: 6.7× Gemini’s cost

The price doubling also applies to Gemini 3.6 Flash and 3.7 Flash, so there is no cheaper Gemini Flash fallback at the same introductory rates.

See the Claude Fable 5.1 pricing guide and GPT-5.6 pricing guide for batch and cache tiers.

Measure cost per task, not just cost per token

Google says Gemini 3.8 Flash “can use more tokens on longer running and complex tasks, by design.”

On difficult problems, the model may:

Take smaller reasoning steps
Verify intermediate results
Call tools iteratively

Artificial Analysis measured the impact:

Gemini 3.8 Flash setting	Cost per task
High	$0.58
Medium	$0.41
Low	$0.24

At high, Gemini used an average of 48,000 output tokens per task—30% more than Gemini 3.7 Flash. As a result, its measured cost rose from $0.40 per task for 3.7 Flash to $0.58 for 3.8 Flash at unchanged token prices.

Two conclusions follow:

A 13.3× per-token gap does not guarantee a 13.3× bill reduction. A premium model may finish with fewer tokens or fewer tool turns.
On Gemini, thinking_level is a major cost control. Moving from high to low reduced measured cost by more than half.

Artificial Analysis has not published comparable per-task figures for Fable 5.1 or Sol in the available text. Benchmark your own prompts before assuming any multiplier.

The Gemini 3.8 Flash thinking-levels guide explains how to configure the setting per endpoint. In some routes, Gemini 3.7 Flash may still be the better buy because its measured per-task cost is 31% lower; see the 3.8 Flash vs. 3.7 Flash comparison.

When to choose each model

Choose Gemini 3.8 Flash for volume and cost-sensitive agents

Use Gemini 3.8 Flash as the default for thousands of agent tasks per day. It matches the premium models on the independent index, leads Google’s finance and legal agent rows, and remains substantially cheaper per token after the January 2027 price increase.

Additional advantages include:

Native video, audio, and PDF inputs
Batch processing at 50% off
5,000 free Google Search grounding requests per month
A free tier for prototyping

Trade-offs:

Text-only output
No Live API
65,536-token output ceiling
Higher token usage on complex tasks

Set thinking_level to medium or low where the workload does not require high-depth reasoning.

Choose Claude Fable 5.1 for the hardest long-horizon reasoning

Fable 5.1 is an escalation model rather than the default starting point. Use it when an incorrect result costs more than the additional tokens, such as:

Multi-hour research agents
Long terminal sessions
Reasoning chains where cheaper models fail your evaluations

Its 128K output limit and $0.25 cache reads are useful for prefix-heavy agent loops that reuse a large context on every turn. In those workloads, the effective cost multiplier can be much smaller than 13.3×.

See what Claude Fable 5.1 is for its own specification and positioning.

Choose GPT-5.6 Sol for OpenAI-based agent stacks

Sol scores 59 at xhigh, ties Gemini 3.8 Flash on HLE-Verified, and offers 128K output at $5 input and $30 output per million tokens.

It is a practical premium option when your agents, evaluations, and tooling already use OpenAI’s ecosystem. The downside is its $0.50 cache-read price, the highest of the three. Sol is therefore a better fit for fresh-context workloads than for long cached sessions.

The Grok 4.6 vs. GPT-5.6 vs. Claude Fable 5 comparison provides additional context on Sol versus the previous Anthropic flagship.

Test all three in one Apidog workspace

Every model argument should end with the same action: measure your own prompts.

Apidog is an API client and testing platform. It does not run these models, but it makes it fast to send the same request to all three providers and compare the results.

1. Create three environments

Create one project with three environments. Store each provider’s base URL and API key as environment variables:

Environment	Base URL variable	API key variable
Gemini	`https://generativelanguage.googleapis.com`	`GEMINI_API_KEY`
Claude	Anthropic’s base URL	Your Claude key
OpenAI	OpenAI’s base URL	Your OpenAI key

Keep keys in environment variables—not in request bodies or shared collections.

2. Create one test scenario

Add three request steps with the same prompt.

The Gemini request uses /v1beta/interactions with:

{
  "model": "gemini-3.8-flash",
  "thinking_level": "medium"
}

The Claude and OpenAI steps should use their respective messages or chat endpoints.

Add assertions to every step:

HTTP status is 200
A JSON path confirms that an answer is present
The token-usage field stays below your cost ceiling

For Gemini’s legacy endpoint, the reasoning-token field is:

usageMetadata.thoughtsTokenCount

Assert against the equivalent usage block for the other providers.

3. Run and schedule the comparison

Run the scenario against a set of golden prompts, then schedule it daily. If a vendor changes a default or a model starts consuming more thinking tokens, the failed assertion alerts you before the invoice does.

Use the AI agent API testing guide for multi-turn scenarios and scheduling API tests in Apidog for recurring runs.

Download Apidog to create the three at $0.75 input and $3.75 output through December 31, 2026.

Artificial Analysis measured its cost per task at:

$0.58 at high
$0.41 at medium
$0.24 at low

Because token usage varies by model and prompt, measure cost per completed task—not just cost per token.

Do all three models have a 1M-token context window?

Yes:

Gemini 3.8 Flash: 1,048,576 input tokens
Claude Fable 5.1: 1M tokens
GPT-5.6 Sol: 1M tokens

Maximum output differs. Gemini supports 65,536 tokens, while the other two support 128K.

Why is Claude Fable 5.1 missing from Google’s benchmark tables?

Google’s published rows list Claude Opus 5 and Claude Sonnet 5. Fable 5.1 shipped on September 1, one day before Gemini 3.8 Flash, so it was not included.

For Anthropic’s own results, see the Claude Fable 5.1 vs. Opus 5 comparison.

Does Gemini’s price advantage survive 2027?

Partly. On January 1, 2027, Gemini 3.8 Flash moves to $1.50 input and $7.50 output per million tokens.

It remains cheaper, but the gap falls to:

Fable 5.1: 6.7× Gemini on both input and output
Sol: 3.3× Gemini on input and 4× on output

Which model should you call first?

Start with Gemini 3.8 Flash for high-volume workloads. Set thinking_level per route and monitor tokens per task.

Escalate to Claude Fable 5.1 when cheaper models fail your evaluations, especially when you reuse a large context across turns. Choose GPT-5.6 Sol when the rest of your stack is already OpenAI-based and you want a premium model without adding another vendor.

Before committing, send the same golden prompts to all three models in one test scenario. The price ratio is public; the cost-per-task ratio for your prompts is yours to measure.

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