Verified October 2026

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GPT-6.1 Sol vs Gemini 3.8 Flash

A straight cost comparison of GPT-6.1 Sol (OpenAI) and Gemini 3.8 Flash (Google Gemini), per 1,000,000 tokens, verified October 2026.

$2GPT-6.1 Sol input / 1M
$0.75Gemini 3.8 Flash input / 1M
63%Blended price gap
Gemini 3.8 FlashCheaper blended

Rate-by-rate comparison

USD per 1M tokens · October 2026
MetricGPT-6.1 SolGemini 3.8 FlashWinner
Input / 1M$2$0.75Gemini 3.8 Flash
Cached input / 1M$0.10$0.075Gemini 3.8 Flash
Cache write / 1M$2.50—Gemini 3.8 Flash
Output / 1M$10$3.75Gemini 3.8 Flash
Context window1,050,0001,000,000GPT-6.1 Sol
Batch APIYesYesTie
Long-context tier> 272,000> 200,000—

For per-token metrics, the lower number wins. For context window and batch support, the higher or broader option wins. A dash means the provider does not publish that rate: a missing cache-write fee means writes are not billed per token, while a missing cached-input rate means cache hits bill at the full input rate.

Blended cost: 1M input + 250K output

ModelBlended costDifference
GPT-6.1 Sol$4.50+$2.81
Gemini 3.8 Flash$1.69Cheaper

Ten workloads, two bills

The same two models, priced across ten workloads that differ only in their input:output ratio. Nothing else changes — no retries, no system prompt, no middleware.

WorkloadGPT-6.1 SolGemini 3.8 FlashWinner
Classification
100K reqs · 500 in / 10 out
$110$41.25Gemini 3.8 Flash
Data extraction
10K reqs · 2K in / 200 out
$60$22.50Gemini 3.8 Flash
Support chat
1K reqs · 4K in / 600 out
$14$5.25Gemini 3.8 Flash
Code review
2K reqs · 8K in / 1.5K out
$62$23.25Gemini 3.8 Flash
Summarisation
1K reqs · 20K in / 1K out
$50$18.75Gemini 3.8 Flash
Agent loop
5K reqs · 12K in / 2K out
$220$82.50Gemini 3.8 Flash
Translation
500 reqs · 15K in / 15K out
$90$33.75Gemini 3.8 Flash
Long-document QA
200 reqs · 120K in / 800 out
$49.60$18.60Gemini 3.8 Flash
Cached RAG
1K reqs · 50K in (45K cached) / 1K out
$24.61$10.91Gemini 3.8 Flash
Nightly batch
10K reqs · 10K in / 2K out
$200$75Gemini 3.8 Flash

GPT-6.1 Sol wins 0 of 10, Gemini 3.8 Flash wins 10 of 10. The winner is set by the token mix, not by the headline rate.

Where the two bills cross over

Gemini 3.8 Flash is cheaper on both sides of the mix: $0.75 vs $2 per million input tokens, and $3.75 vs $10 per million output. No input:output ratio reverses that, so on price alone the other model never becomes the better choice — it has to win on context, latency or output quality.

Cost at four scales

The same support-chat workload (4K input, 600 output per request) at four monthly volumes.

Requests / monthGPT-6.1 SolGemini 3.8 FlashDifference
100$1.40$0.525Gemini 3.8 Flash cheaper by $0.875
1,000$14$5.25Gemini 3.8 Flash cheaper by $8.75
10,000$140$52.50Gemini 3.8 Flash cheaper by $87.50
100,000$1,400$525Gemini 3.8 Flash cheaper by $875

What caching does to each bill

Caching only helps the part of the prompt that repeats. This is the support-chat workload (1,000 requests, 4,000 input tokens each) with an increasing share of that prompt served from a warm cache, written once on the first request.

Cached share of the promptGPT-6.1 SolGemini 3.8 FlashWinner
0%$14$5.25Gemini 3.8 Flash
50%$10.20$3.90Gemini 3.8 Flash
80%$7.93$3.09Gemini 3.8 Flash
95%$6.79$2.69Gemini 3.8 Flash

Both models publish a cached-read rate, so the discount is comparable: GPT-6.1 Sol reads cache at $0.10 per million, Gemini 3.8 Flash at $0.075.

Which one should you pick?

GPT-6.1 Sol wins on none of the ten workloads above, while Gemini 3.8 Flash wins on classification, data extraction, support chat, code review, summarisation, agent loop, translation, long-document qa, cached rag, nightly batch.

Beyond price, the two differ on input rate ($2 vs $0.75); output rate ($10 vs $3.75); cached-input rate ($0.10 vs $0.075); cache-write rate ($2.50 vs —); context window (1,050,000 vs 1,000,000).

Batch mode is available on GPT-6.1 Sol and Gemini 3.8 Flash, which halves the nightly job. Both publish cached-input discounts, so a stable system prompt is worth caching on either.

FAQ

Is GPT-6.1 Sol cheaper than Gemini 3.8 Flash?

On a blended 1M-input-plus-250K-output workload, Gemini 3.8 Flash is cheaper by 63% ($1.69 vs $4.50). The gap changes for cache-heavy prompts and for batch jobs, as the workload table shows.

Which handles longer prompts, GPT-6.1 Sol or Gemini 3.8 Flash?

GPT-6.1 Sol has the larger context window (1,050,000 vs 1,000,000 tokens). Watch the long-context threshold on GPT-6.1 Sol and Gemini 3.8 Flash: rates rise once input passes it.

Do both models support prompt caching?

GPT-6.1 Sol bills cached reads at $0.10 per 1M; Gemini 3.8 Flash at $0.075.

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