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Gemini 3.8 Flash vs Inkling

Published LiveBench scores across all seven categories, live list pricing, context windows, and the measured cost of a point of capability — for both models, side by side.

OpenRouter + LiveBenchAll comparisonsFull leaderboard

Gemini 3.8 Flash is the better model, at roughly the same price.

Gemini 3.8 Flash leads by 15.4 points overall and the two list within about 10% of each other, so the cheaper-but-weaker trade-off does not apply. Price parity plus a score gap usually makes this an easy call. Gemini 3.8 Flash also leads on measured cost per point of capability, at $0.7520 per point.

google

Gemini 3.8 Flash

Blended / 1M
$1.65
Context
1.0M
Released
Sep 2, 2026
Overall score
67.3
reasoningtool callingimage inputvideo inputfile inputaudio inputprompt caching

thinkingmachines

Inkling

Blended / 1M
$1.76
Context
524K
Released
Jul 17, 2026
Overall score
51.9
reasoningtool callingimage inputaudio inputprompt caching

Specs and pricing

MetricGemini 3.8 FlashInkling
LiveBench overall

Mean of the seven LiveBench category scores, 0–100. Higher is better.

67.3win51.9
Cost per point

Measured benchmark spend divided by overall score — dollars per point of capability.

$0.7520win$1.19
Blended price / 1M

3:1 input:output mix, the usual shape of production traffic.

$1.65$1.76
Input price / 1M$0.825win$1.00
Output price / 1M$4.13$4.05
Cached input / 1M

Price of an input token served from the prompt cache, where the provider publishes one.

$0.083win$0.170
Context window1.0Mwin524K
Max output tokens66K472Kwin

Benchmarks by category

An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Gemini 3.8 Flash on top, Inkling below, both out of 100.

Coding
82.2
64.0
Reasoning
89.3
78.3
Data analysis
54.0
72.8

What each one costs to run

Per-token prices are hard to feel. These are monthly list costs for both models across five workload shapes, using each provider's published cached-input rate where there is one.

WorkloadGemini 3.8 FlashInkling
Support chatbot

1.2K in / 400 out × 200K requests

$474.54/mo$504.24/mo
RAG assistant

8K in / 600 out × 100K requests

$610.50/mo$711.00/mo
Coding agent

40K in / 4K out × 20K requests

$574.20/mo$659.20/mo
Document extraction

20K in / 1.5K out × 50K requests

$1,097/mo$1,262/mo
Bulk classification

500 in / 20 out × 5M requests

$2,104/mo$2,490/mo
Run these two through the cost calculator

Which should you pick?

You are running this at volume

Gemini 3.8 Flash

Lowest measured cost per point of capability at $0.7520 per point — the gap compounds with every request.

Quality matters more than the bill

Gemini 3.8 Flash

Highest overall LiveBench score of the two at 67.3.

The workload is coding or agentic work

Gemini 3.8 Flash

Leads on coding — 82.2 against 64.0.

You need to fit large documents in one call

Gemini 3.8 Flash

Wider context window — 1.0M against 524K.

Gemini 3.8 Flash vs Inkling FAQ

Which is better, Gemini 3.8 Flash or Inkling?

Gemini 3.8 Flash is the better model, at roughly the same price. Gemini 3.8 Flash leads by 15.4 points overall and the two list within about 10% of each other, so the cheaper-but-weaker trade-off does not apply. Price parity plus a score gap usually makes this an easy call. Gemini 3.8 Flash also leads on measured cost per point of capability, at $0.7520 per point.

Is Gemini 3.8 Flash cheaper than Inkling?

They cost about the same. Both land near $1.65 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.

Gemini 3.8 Flash vs Inkling: which scores higher on benchmarks?

Gemini 3.8 Flash scores 67.3 and Inkling scores 51.9 overall on LiveBench, the mean of its seven categories. That is a 15.4-point lead for Gemini 3.8 Flash. Category scores differ from the overall figure — a model can lead on reasoning and trail on coding, which the per-category table above breaks out.

Which gives better value for money, Gemini 3.8 Flash or Inkling?

Gemini 3.8 Flash. Cost per point divides the measured dollars LiveBench spent running the benchmark by the score it earned, so it captures something token pricing misses: a reasoning model can emit many times more tokens than its per-token rate suggests. Gemini 3.8 Flash works out at $0.7520 per point and Inkling at $1.19.

Does Gemini 3.8 Flash or Inkling have a bigger context window?

Gemini 3.8 Flash has the larger context window: 1.0M for Gemini 3.8 Flash against 524K for Inkling. Note that a window you can fill is not a window you should fill — retrieval quality usually degrades well before the limit, and you pay for every token you put in it.

Do Gemini 3.8 Flash and Inkling support prompt caching?

Both publish a cached-input rate: $0.083 per million for Gemini 3.8 Flash and $0.170 for Inkling, against full input rates of $0.825 and $1.00. On a workload with a long stable prefix — a system prompt, a tool schema, a retrieved corpus — that changes the economics more than the headline price does.

Related comparisons

How these numbers are produced

  • Price — provider list price from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
  • Scores — LiveBench release 2026-09-02, using their own category map. Each model shows its strongest published run. A blank means “not evaluated”, never “bad”.
  • Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
  • “Win” — awarded only past a threshold: one full point on a benchmark score, 10% on a price, 25% on a context window. Anything tighter reports as a tie, because effort settings alone move a LiveBench score by more than that.

Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.