GPT vs Claude vs Gemini: Task-Based Comparison with Real Usage Data and Live API Pricing (2026)

Short answer: Claude (Opus 5, Sonnet 5) leads for coding and deep analysis, GPT-5.6 for writing and general-purpose production, and Gemini Flash for speed and high-volume workloads. Our platform's real traffic over the last 30 days tells the same story: volume work concentrates on Gemini Flash-class models, while Claude requests skew heavily toward code and analysis.

Most "GPT vs Claude vs Gemini" comparisons online pit ChatGPT Plus against Claude Pro and Gemini Advanced — chat subscriptions — and wave at the API side in two sentences. This guide does the opposite: we compare the three families from a developer's perspective, task by task — coding, writing, analysis, speed, cost, vision. And instead of recycled benchmark chatter, the evidence is our own platform data: live sale prices (with Turkish lira equivalents), the real usage distribution of the last 30 days, and measured response times from the last 14 days.

The goal is not to crown a winner; all three families are production-grade, and the right question is not "which is best" but "which is best for this task". For a family-by-family deep dive, see our AI model families guide; this article focuses on the head-to-head comparison and the decision matrix.

Which Model for Which Task? The Decision Matrix

The task-level summary: Claude for code and analysis, GPT-5.6 for writing, Gemini Flash for speed and volume. The matrix below shows our first pick and the strong alternative across six task axes:

Task	First pick	Strong alternative	Why
Code generation and refactoring	Claude Opus 5 / Sonnet 5	GPT-5.6 Sol	Strong code quality and multi-step agent flows; the developer tooling ecosystem largely formed around Claude
Content and writing	GPT-5.6 Terra / Sol	Claude Sonnet 5	Fluent text with strong tone control; tiered pricing options
Long documents and analysis	Claude Opus 5 (1M context)	Gemini 3.1 Pro	Consistent, careful analysis over wide context
Real-time / low latency	Gemini 3.6 Flash	GPT-5.6 Luna	The Flash class is built for the speed/cost equation
Cost-sensitive high volume	GPT-5.6 Luna / Gemini Flash	DeepSeek V4 Flash	Sub-dollar pricing per 1M tokens
Vision input (multimodal)	Gemini family	GPT-5.6	Gemini carries the strongest multimodal emphasis

The matrix assumes quality-first. When budget takes priority, stepping one tier down within the same family (Sonnet instead of Opus, Terra instead of Sol) is enough for most production pipelines. For teams willing to look beyond the big three, the DeepSeek and Kimi notes are below.

How Do the Three Split on Coding, Writing, and Analysis?

The clearest split: Claude leads on code and deep analysis, GPT-5.6 on fluent writing, while Gemini positions itself on multimodal and volume work.

On code, the Claude family plays two strong cards. Claude Opus 5, with its 1M-token context, can take large codebases in a single request — the first pick for complex refactors, architecture reviews, and multi-step agent flows. Claude Sonnet 5, at $3 input / $15 output per 1M tokens (against Opus at $7.50/$37.50), is the workhorse for day-to-day coding loads. The developer tooling ecosystem — Claude Code and similar — also formed largely around this family. We see the same pattern in our own traffic: Claude requests on our platform skew heavily toward code and analysis workloads. Note: Onysoft is a member of the Anthropic Claude Partner Network; we provide Claude access under that official partnership.

On writing and content, the GPT-5.6 family ships in three tiers: Sol as the highest-capacity tier, Terra as the balanced mid-option for everyday content production, and Luna as the light, fast end. Fluent prose with flexible tone and format control is this family's well-known strength — a safe harbor for marketing copy, product descriptions, and general-purpose assistants.

Long-document analysis is Claude Opus 5 territory; Gemini 3.1 Pro is the alternative with wide context and multimodal capability — it ranking second in our 30-day usage data with 1,981 requests is no coincidence. On vision input, all three families accept images; Gemini carries the strongest multimodal emphasis. Image generation, however, is not this trio's job — that belongs to the dedicated image models in the catalog.

Where Do Speed and Cost Land? (With DeepSeek and Kimi Notes)

On speed, the Gemini Flash class leads; on cost, GPT-5.6 Luna and DeepSeek V4 trade blows; and Kimi K3 is the flagship of the open-weight camp.

Speed: For real-time assistants, live chat, and streaming scenarios, Flash-class models — led by Gemini 3.6 Flash — are built for low latency, with GPT-5.6 Luna a serious alternative in the same class. Our platform-wide measurement gives a sense of scale: across the last 14 days, 6,959 successful requests averaged 3.8 seconds response time, with the fastest at 0.3 seconds. Light models with short prompts run well below that average.

Cost: The cheapest member of the three families is GPT-5.6 Luna ($0.15 input / $0.90 output per 1M tokens). Step outside the trio and DeepSeek V4 Flash asks $0.21 input / $0.42 output — Luna wins on input, DeepSeek on output, so output-heavy work (long answers, code generation) usually favors DeepSeek. One tier up, DeepSeek V4 Pro ($0.65/$1.31) still stays in the sub-dollar class.

Kimi K3 note: If open weights are a requirement, or you need agent automation plus long context, Kimi K3 with its 1M-token context ($4.50/$22.50) is among the strongest candidates outside the trio — details in our Kimi K3 API guide. With models from more than 60 providers reachable through the same key, "one of the big three" is never an obligation.

GPT vs Claude vs Gemini API Pricing: The Live Table (with TRY Equivalents)

The pricing comparison speaks plainly: within a family, tiers differ by up to 50x; across families, the flagships price close to each other. The table below shows current sale prices on our platform (per 1M tokens):

Model	Input ($/1M)	Output ($/1M)	Input (TRY/1M)	Output (TRY/1M)	Context
anthropic/claude-opus-5	$7.50	$37.50	356.66 TL	1,783.31 TL	1,000,000
anthropic/claude-sonnet-5	$3.00	$15.00	142.67 TL	713.33 TL	1,000,000
anthropic/claude-haiku-4.5	$1.50	$7.50	71.33 TL	356.66 TL	200,000
openai/gpt-5.6-sol	$7.50	$45.00	356.66 TL	2,139.98 TL	1,050,000
openai/gpt-5.6-terra	$1.50	$9.00	71.33 TL	428.00 TL	1,050,000
openai/gpt-5.6-luna	$0.15	$0.90	7.13 TL	42.80 TL	1,050,000
google/gemini-3.6-flash	$2.25	$11.25	107.00 TL	534.99 TL	1,048,576
deepseek/deepseek-v4-flash	$0.21	$0.42	9.99 TL	19.97 TL	1,048,576
deepseek/deepseek-v4-pro	$0.65	$1.31	30.91 TL	62.30 TL	1,048,576
moonshotai/kimi-k3	$4.50	$22.50	214.00 TL	1,069.99 TL	1,048,576

Measured: August 5, 2026 — api.onysoft.com live catalog. TRY equivalents calculated at the August 5, 2026 TCMB rate (1 USD = 47.555 TRY).

Three practical takeaways: (1) In the flagship class, Claude Opus 5 and GPT-5.6 Sol share the same input price ($7.50), with Opus cheaper on output ($37.50 vs $45.00). (2) In the mid-tier, GPT-5.6 Terra matches Claude Haiku 4.5 on input price, while Gemini 3.6 Flash sits slightly above both but carries a 1M context. (3) The bulk of any invoice almost always comes from output tokens — compare the output column first. To run the numbers on your own volume, the cost calculator is ready.

Platform Data: What Did Developers Actually Use in the Last 30 Days?

Real usage speaks more honestly than surveys. The top 10 models by requests through our platform over the last 30 days:

#	Model	Requests (last 30 days)
1	google/gemini-3.5-flash-lite	4,551
2	google/gemini-3.1-pro-preview	1,981
3	google/gemini-3-flash-preview	1,372
4	anthropic/claude-opus (latest alias)	296
5	google/gemini-3.6-flash	284
6	anthropic/claude-sonnet (latest alias)	141
7	perplexity/sonar-pro	104
8	google/gemini-3.1-flash-lite	94
9	anthropic/claude-opus-5	73
10	anthropic/claude-haiku-4.5	48

Measured: August 5, 2026 — api.onysoft.com live catalog (last 30 days, top 10 across the full catalog).

Two observations stand out. First, of the 8,944 requests in the top 10, 6,301 — roughly 70 percent — went to Gemini Flash-class models: classification, summarization, and high-volume production traffic effectively runs on Flash. Second, Claude weighs in on the nature of the work rather than the request count: the four Claude entries (558 requests in total) skew heavily toward code and analysis — fewer but longer, context-heavy requests. GPT not appearing in this period's top 10 does not mean "worse model"; it shows that on our platform the volume end concentrates on Gemini and the quality end on Claude. On response time we have measurements too: across the last 14 days, 6,959 successful requests averaged 3.8 seconds, the fastest at 0.3 seconds.

How It Works on Onysoft: All Three Families Behind One API

The practical conclusion of this comparison: the right architecture is not picking one model, it is routing each task to the right model — and that does not require three accounts and three invoices. On Onysoft AI Gateway you create a free account, generate an sk-ony- prefixed key from the dashboard, and add balance (billed in Turkish lira at the TCMB rate). One OpenAI-compatible endpoint reaches 708+ models with the same key; switching families is nothing more than changing the model parameter:

curl https://api.onysoft.com/v1/chat/completions \
  -H "Authorization: Bearer sk-ony-YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "anthropic/claude-sonnet-5",
    "messages": [{"role": "user", "content": "Review this function and suggest a refactor."}]
  }'

Setting up task-based routing in code takes a few lines — one client, model chosen by task type:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.onysoft.com/v1",
    api_key="sk-ony-YOUR_KEY",
)

TASK_MODEL = {
    "code":    "anthropic/claude-sonnet-5",   # coding and analysis
    "writing": "openai/gpt-5.6-terra",        # content production
    "volume":  "google/gemini-3.6-flash",     # classification, summaries
}

def ask(task, message):
    r = client.chat.completions.create(
        model=TASK_MODEL[task],
        messages=[{"role": "user", "content": message}],
    )
    return r.choices[0].message.content

Before committing, try all three families side by side with the same prompt in the Playground — the fastest way to settle model selection. And with more than 60 providers in the catalog, moving to next month's new flagship will again be a one-line change.

Last updated: August 5, 2026 · Data: api.onysoft.com live catalog