Kimi K3, Gemini 3.6 Flash and GPT-5.6 Sol all advertise roughly one million tokens of input context, but they are not interchangeable products. Kimi K3 is the disruptive open-weight challenger with strong agentic knowledge-work results. Gemini 3.6 Flash is the low-cost, high-throughput option for multimodal agent loops. GPT-5.6 Sol is the expensive frontier generalist with the broadest first-party tool surface in this comparison.
The simple winner depends on the workload:
Choose Gemini 3.6 Flash when unit cost, speed, multimodal input and frequent tool loops dominate.
Test Kimi K3 when long-horizon coding, large knowledge-work tasks, future weight access or cached long context matters.
Choose GPT-5.6 Sol when the task justifies a premium frontier model and relies on OpenAI's integrated tool and agent ecosystem.
No benchmark makes that decision for you. The models use different harnesses, effort settings and tool stacks. A valid evaluation measures cost and acceptance rate on the same production tasks.
The verified Kimi K3 vs Gemini 3.6 Flash vs GPT-5.6 Sol matrix
Prices are standard API list rates in US dollars per one million text tokens, verified on 23 July 2026. Provider-specific batch, priority, flex, tool-call and long-prompt surcharges are not mixed into the headline comparison.
| Decision factor | Kimi K3 | Gemini 3.6 Flash | GPT-5.6 Sol |
|---|---|---|---|
| Uncached input price | $3.00 | $1.50 | $5.00 |
| Cached input price | $0.30 | Provider caching rules apply; calculate from the current pricing page | $0.50 |
| Output price | $15.00 | $7.50 | $30.00 |
| Advertised input context | 1,048,576 | 1,048,576 | 1,050,000 |
| Maximum output | Not yet clearly standardised in the public launch material | 65,536 | 128,000 |
| Input modalities | Text and native vision | Text, image, video, audio and PDF | Text and image |
| Model status | API available; full weights promised by 27 July | Stable and generally available | Generally available |
| Weight access | Promised, but not available at publication time | Closed | Closed |
| First-party emphasis | Long-horizon coding, knowledge work, reasoning | Fast agentic and multimodal execution | Complex professional work and broad tool use |
| Published self-host guidance | 64-plus accelerator supernode | Not applicable | Not applicable |
| Benchmark signal used here | Independent Intelligence Index 57; AA-Briefcase Elo 1,543 | Independent Intelligence Index 50; substantially shorter task time than 3.5 Flash | Intelligence Index 58.9 in OpenAI's published comparison; independent AA-Briefcase Elo 1,501 at max |
This table is not a ranking. It shows why a team can rationally choose different models for different stages of one system.
Price: Gemini is cheapest, Kimi sits in the middle, Sol charges a frontier premium
For a workload using 10 million uncached input tokens and two million output tokens:
| Model | Input cost | Output cost | Total |
|---|---|---|---|
| Gemini 3.6 Flash | $15 | $15 | $30 |
| Kimi K3 | $30 | $30 | $60 |
| GPT-5.6 Sol | $50 | $60 | $110 |
The arithmetic makes Gemini look decisive, but token prices do not reveal completed-task cost. A model may require more turns, generate longer reasoning, call more tools or fail more often. Artificial Analysis found that Kimi K3 averaged $10.57 per AA-Briefcase task despite its mid-range token price. It used 83 turns and 120,000 output tokens on average.
Caching changes the Kimi and GPT picture for long, reused context. If eight million of the ten million input tokens in the example were cache hits:
Kimi input would cost $2.40 for cached tokens plus $6 for uncached tokens. With the same output, the total would be $38.40.
GPT-5.6 Sol input would cost $4 for cached tokens plus $10 for uncached tokens. With the same output, the total would be $74.
Those are transparent illustrations, not invoices. Cache eligibility, cache writes, minimum retention, prompts above provider thresholds, reasoning tokens and tool calls can change the bill. OpenAI states that prompts above 272,000 input tokens are charged at twice the input rate and 1.5 times the output rate for the full request. That surcharge is especially relevant when a one-million-token window is part of the buying argument.
Context: all three advertise about one million tokens, but the product meaning differs
On paper, context is nearly tied. In practice, the useful window depends on retrieval accuracy, latency, price and how well the surrounding agent manages it.
Kimi K3 is explicitly designed for large repositories and long-running work. Its architecture uses Kimi Delta Attention and a sparse expert system, and Moonshot says it has developed KDA-aware prefix caching for vLLM. That implementation is due with the model release rather than something buyers can validate today.
Google's Gemini 3.6 Flash documentation lists a 1,048,576-token input limit and supports URL context, file search, search grounding and code execution. Its broad input modalities make it attractive when the “context” is a mixture of documents, recordings, images and video rather than text alone.
OpenAI's GPT-5.6 Sol model page lists a 1,050,000-token context and 128,000 maximum output tokens. It supports web search, file search, image generation, code interpreter, hosted shell, computer use, MCP and skills through the Responses API. Its advantage is not the extra 1,424 advertised context tokens; it is the integrated execution surface around the model.
For all three, buyers should test context at staged lengths. A model that accepts a million tokens but loses a requirement in the middle of a document set is less useful than a cheaper model operating on a curated 100,000-token pack.
Benchmarks: Kimi challenges Sol, Gemini optimises the loop
Moonshot says K3's overall performance still trails GPT-5.6 Sol and Claude Fable 5, even as it reports individual wins in coding and engineering. That admission is useful: K3 is not presented as a universal leader.
Independent evidence shows a more nuanced split. Artificial Analysis gives K3 a score of 57 on its Intelligence Index, comparable with GPT-5.5 and Claude Opus 4.8. On AA-Briefcase, K3 reached an Elo of 1,543, ahead of GPT-5.6 Sol at max effort on 1,501. K3 also achieved stronger analytical quality in that evaluation, while GPT-5.6 Sol produced better presentation quality and finished with fewer turns.
OpenAI's release material reports an Artificial Analysis Intelligence Index v4.1 score of 58.9 for GPT-5.6 Sol. The score is close enough to K3 that workflow fit, harness and cost should matter more than declaring a permanent one-point winner.
Gemini 3.6 Flash belongs to a different performance class. Artificial Analysis reports an Intelligence Index score of 50—the same aggregate score it measured for Gemini 3.5 Flash—but found a large reduction in average task time. Google also reports better coding, machine-learning engineering and computer-use results than the prior Flash model. Gemini's proposition is frontier-adjacent capability delivered quickly and cheaply, not maximum score at any cost.
Do not compare a Kimi Code benchmark with a Codex benchmark as if the base models ran in identical conditions. Agent harnesses determine tool selection, memory, retries and stopping. For a buyer, “model plus harness” is often the real product.
Access and deployment: Kimi is the only candidate with promised weights
All three can be used through an API. Gemini 3.6 Flash and GPT-5.6 Sol are closed services. Kimi K3 is also an API service today, but Moonshot says full weights will arrive by 27 July.
That promise creates a strategic distinction. Weight access may allow organisations to control hosting location, tune the runtime, audit behaviour and avoid a single inference provider. It does not make K3 easy or cheap to host. Moonshot recommends 64 or more accelerators in a high-bandwidth supernode. Storage arithmetic alone places even a four-bit representation near 1.4 terabytes before runtime overhead.
Until the files and licence are public, procurement teams should record K3 as “API available, weights pending”. They should not write “self-hosted option available” into a production architecture.
For Gemini, Google offers multiple consumption modes and extensive grounding and multimodal features. For GPT-5.6 Sol, OpenAI's Responses API and Codex integrations create a broad agent platform. Those managed capabilities may be worth more than theoretical weight ownership for teams that do not operate large inference clusters.
Which model should you use for coding?
Kimi K3 is the most intriguing choice for long, autonomous engineering sessions and large repositories. Moonshot reports kernel optimisation, compiler construction and 48-hour autonomous design work. Independent AA-Briefcase testing supports its ability to sustain complex tasks, though the high turn count warns that “persistent” can become slow and expensive.
GPT-5.6 Sol is the safer premium benchmark for teams already using Codex, hosted shell, apply-patch workflows and OpenAI's tool permissions. OpenAI's system card also deserves attention: it reports strong cyber capability and notes rare but important cases where increased persistence went beyond user intent. Strong models still require explicit permissions, sandboxes and destructive-action gates.
Gemini 3.6 Flash is the likely choice for rapid coding loops, high request volume and mixed visual inputs. It may be ideal as the routine executor while a more expensive model handles escalations.
The best architecture may route rather than standardise: Gemini for high-volume triage, Kimi for long knowledge or code tasks, and Sol for premium complex work inside an OpenAI tool stack.
Which model should you use for knowledge work?
Kimi K3's AA-Briefcase result makes it a serious candidate for financial models, reports, presentations and document-heavy research. Its analytical score was particularly strong. Yet it took nearly an hour per task on average and produced weaker presentation output than Sol.
GPT-5.6 Sol is better suited when polished deliverables, integrated tools and high reliability justify the cost. The 128,000-token output ceiling also gives it room for unusually large generated artefacts, although good systems should still constrain unnecessary output.
Gemini 3.6 Flash fits workflows where many documents, images, audio or video inputs need to be processed quickly. It is the cost leader in the worked example and can use Google search, Maps and URL grounding.
For an executive research workflow, compare these measures:
factual acceptance rate after source checking;
spreadsheet, slide or code correctness;
total human editing minutes;
end-to-end completion time;
total cost including retries and tools;
sensitive-data and regional-processing requirements.
A seven-step production bake-off
Use the same evaluation contract for all three models:
Select at least 30 representative tasks, including routine, difficult and failure-prone examples.
Fix the source documents, tools, system instructions and maximum turn budget.
Use comparable effort settings rather than max on one model and default on another.
Score outputs with objective checks before subjective preference.
Record input, cached input, output, tool-call and retry costs.
Test permission boundaries, prompt injection and destructive-action refusal.
Route by measured workload class instead of selecting one global winner.
Our Gemini 3.6 Flash versus 3.5 Flash analysis gives more detail on Google's efficiency change. The GPT-5.6 workload router explains when OpenAI's less expensive Terra or Luna tiers may be more rational than Sol. The GPT-5.6 family pricing guide provides the wider OpenAI cost ladder.
Final decision
Gemini 3.6 Flash wins the list-price and throughput case. GPT-5.6 Sol wins the premium integrated-tool case. Kimi K3 wins the strategic-interest case because it pairs near-frontier evidence with promised weight access, while landing between the two on token price.
The wrong decision is to purchase a benchmark rank. The right decision is to test completed work under one harness, measure cost per accepted result and preserve a routing option. Kimi K3's arrival makes that multi-model strategy more attractive, not less.
