GPT 5.6 Sol vs Kimi K3: API Pricing and Benchmarks (2026)

Two flagship models launched a week apart in July 2026, both aimed at coding agents that run for hours without a human in the loop. GPT 5.6 Sol is OpenAI's top variant, with a parallel-agent mode and the highest published Coding Agent Index score. Kimi K3 is Moonshot AI's 2.8-trillion-parameter flagship, with a 1M-token window and open weights promised. On crowd voting they sit less than one Elo point apart. On everything a coding agent actually feels, the gap is much wider.

TL;DR: GPT 5.6 Sol wins on throughput and control: 80 on the Coding Agent Index (the top score), 88.8% Terminal-Bench 2.1 rising to 91.9% in ultra mode, adjustable reasoning effort, and up to 750 tokens/sec on Cerebras hardware. Kimi K3 answers with a 1M-token context, 131k default output, the strongest published web-research scores, and weights due July 27. On Arena's English Text board K3 sits at #10 with 1486 Elo and Sol-xhigh at #11 with 1485, close enough to call even. On price they nearly swap places: Sol is cheaper going in at $1.50 per 1M input tokens against K3's $2.10, K3 is cheaper coming out at $10.50 against $12. Both are live on Unifically today through the same endpoint.

The two models in one minute

GPT 5.6 Sol became generally available July 9, 2026 as the top capability level of the GPT 5.6 family. It takes text and image input, holds 1,050,000 tokens of context, returns up to 128,000, and adds two controls this generation: a max reasoning effort above xhigh, and an ultra mode that coordinates four agents in parallel behind one call.

Kimi K3 arrived July 16, 2026: 2.8 trillion parameters with 16 of 896 experts active per pass, a 1,048,576-token window, and 131,072 output tokens by default that can be raised to the full million. Thinking is always on and reasoning_effort accepts only max today. It reads images and video natively.

Same job, opposite bets. Sol bets on orchestration and speed. K3 bets on scale, context, and eventually letting you run the weights yourself.

Coding benchmarks

Read this table with one caveat: Sol's numbers come from OpenAI's release testing and K3's from Moonshot's, each on their own harness. Terminal-Bench in particular moves several points between harnesses, so treat a sub-point gap as a tie.

The honest summary: where both were measured on the same test, Sol leads DeepSWE by five points and ties Terminal-Bench. Where only one was measured, each looks unbeatable on its own turf, which is exactly what self-published benchmark tables are designed to do. Artificial Analysis, which tests independently, puts Sol top of its Coding Agent Index at 80 and K3 fourth of 187 models on its Intelligence Index at 57.

The Arena line deserves an asterisk too. K3's 1486 comes from roughly 3,600 votes and the board still marks the rating preliminary, against 6,200 for Sol-xhigh; K3 has already slid from #6 in launch week to #10 as votes accumulated. A sub-point gap on those sample sizes is noise, not a ranking.

Speed, and why it decides most agent builds

This is the gap the benchmark tables hide. Artificial Analysis measures Kimi K3 at 62 output tokens per second, below the median for its class, and every K3 request pays full reasoning cost because max is the only effort setting. OpenAI is serving Sol on Cerebras hardware at up to 750 tokens per second for select customers, and Sol's effort dial goes down as well as up.

For a coding agent, that compounds. An agent loop that plans, edits, runs tests, and reads output might make forty model calls to close one ticket. At K3's throughput with mandatory max reasoning, that is a long wait and a large token bill on steps that never needed deep thought. Sol lets you spend reasoning where it matters and run cheap elsewhere.

Ultra mode widens it further. Four agents in parallel lift Terminal-Bench 2.1 from 88.8% to 91.9%, and on SEC-Bench Pro a 16-agent setup takes Sol from 71.2% to 76.2% while cutting time to result. Each agent spends its own tokens, so ultra costs several times a standard call: use it on the hard tickets, not the whole queue.

GPT 5.6 Sol vs Kimi K3 pricing

Both bill per token on Unifically, quoted per million:

Sol is cheaper on the way in, K3 on the way out, so the winner depends on your output ratio. That is exactly where the reasoning setting from the last section lands on the invoice: reasoning tokens bill as output, and K3 runs max on every request. Its 12% output discount only pays off if it is not spending that margin thinking about calls that never needed it.

Put numbers on one agent step. Sending 50,000 input tokens and getting 5,000 back costs $0.135 on Sol and $0.158 on K3. Across the forty-odd calls it takes to close a ticket, that is a real but survivable gap, and smaller than it feels. The throughput difference costs you more than the token price does.

Cache reads matter more here than the headline rates suggest. An agent loop resends a growing transcript every turn, so cached input becomes the largest line on the bill long before output does, and at $0.15 against $0.21 per million Sol has the cheaper one.

Where Kimi K3 wins

• Web research inside the agent loop. K3 posts 91.2% on BrowseComp, the strongest published score. If your agent has to find current facts rather than recall them, that matters more than a Terminal-Bench point.
• Long autonomous runs. 42.0 on SWE Marathon is K3's best result anywhere, and the test is exactly the shape of overnight, minimally supervised engineering work.
• One very long artifact. 131k output tokens by default, raisable to 1M, against Sol's 128k ceiling. For a generated migration, a full report, or a giant patch, K3 does in one response what Sol needs stitching to produce.
• Open weights, if they land. Moonshot committed to publishing K3's weights by July 27, 2026. Nothing is published yet. If it happens, K3 becomes the only model here you can self-host, fine-tune, or run under strict data rules, though 2.8T parameters means a multi-node deployment.

Note what K3 does not give you on the platform: image input. Sol takes text and images through the same endpoint, while K3 runs text in, text out. If your agent reads screenshots or design mocks, that decides it before any benchmark does.

Two behaviors to plan around

K3 is sensitive to thinking history. Moonshot trained it in a preserved-history mode, so a harness that drops earlier reasoning content, or a session switched to K3 mid-run from another model, can destabilise output quality. If your agent framework trims context aggressively, test that before committing.

K3 also acts rather than asks. On an ambiguous instruction it makes a decision and proceeds instead of checking in, so constraints belong in the system prompt rather than in follow-up corrections. For an autonomous agent that trait cuts both ways: fewer stalls, more unrequested changes.

How to call both

Both speak the OpenAI-compatible Chat Completions format, so one request shape covers them and trying the other is a one-word change:

curl -X POST https://api.unifically.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "openai/gpt-5.6-sol",
    "messages": [
      {"role": "user", "content": "Refactor this function and explain what changed."}
    ]
  }'

Swap "model" to "moonshotai/kimi-k3" to run the identical request against K3. Sol accepts reasoning_effort below max, which is where the cost control described above actually lives; K3 takes only max today. Both models also work through /v1/responses, and Claude-family models additionally accept /v1/messages.

Which one should you use?

Pick GPT 5.6 Sol for most coding agents. It leads the independent coding index, matches K3 on terminal work, runs an order of magnitude faster, costs less on input and cached reads, and lets you tune reasoning per request instead of paying max on every call. Ultra mode gives you a second gear for the hardest tickets.

Pick Kimi K3 when the job is long, research-heavy, or output-heavy: overnight runs on a large repo, agents that browse the live web, or single responses that need to exceed 128k tokens. Choose it too if an open-weights path matters to your roadmap.

Both run on Unifically through the same OpenAI-compatible /v1/chat/completions endpoint, so trying the other one is a model-id change: openai/gpt-5.6-sol or moonshotai/kimi-k3. Browse every language model on the platform to compare the rest.

We will update this comparison when K3's weights land, when its lower reasoning-effort modes arrive, and when independent benchmarks put both models on the same harness.