Kimi K3.1 Pro vs DeepSeek V4.1 Flash, Fable 5.1, and GPT-6
Where does Kimi K3.1 Pro fit?
Kimi K3.1 Pro belongs on the shortlist for teams seeking a capable coding and research model at a lower base token price than Claude Fable 5.1 or GPT-6 Astra. DeepSeek V4.1 Flash offers a substantially cheaper starting point for high-volume work. The premium models need to earn their additional cost through better accepted results on the tasks that matter to your application.
This comparison uses GPT-6 Astra, specifically gpt-6-astra. "GPT-6" describes a family and should not be treated as one interchangeable model or price. The same care applies to DeepSeek's serving alias and Kimi's separate Highspeed variant.
Official API documentation, prices, and benchmark sources were checked on October 29, 2026. The selection advice is an engineering interpretation of that evidence. The cost examples are calculations, not measured model runs.
First, identify the model that will receive the request
| Model | Direct API identity | A detail that affects adoption |
|---|---|---|
| Kimi K3.1 Pro | kimi-k3.1-pro | 1,048,576-token context; thinking is always enabled |
| DeepSeek V4.1 Flash | deepseek-flash, currently serving V4.1-Flash-1017 | The public alias can serve a newer checkpoint than its old name suggests |
| Claude Fable 5.1 | claude-fable-5-1 | Very low cache-read rate relative to its base input price |
| GPT-6 Astra | gpt-6-astra | 1,050,000-token context; higher rates above 272K input tokens |
Moonshot's K3.1 launch announcement describes a 3T-class model with native visual capabilities and stronger coding, 3D, and financial-research work. API access is available, while the announcement schedules full model weights for release by November 4. On the date of this article, that is a release commitment, not confirmation that the weights have already shipped.
Kimi also offers kimi-k3.1-pro-highspeed, which Moonshot says delivers two to three times the speed. Treat that as a vendor claim to test under your context size and concurrency, and associate it with the Highspeed rate card.
DeepSeek's current pricing and model page says the legacy deepseek-v4-flash and deepseek-v4-flash-vision-exp names now route to V4.1-Flash-1017. Keeping an old string in a configuration file does not necessarily pin the underlying model.
Published coding results: useful, but not one controlled race
Kimi's launch report gives K3.1 Pro 65.7% on Terminal-Bench 4.0, using Claude Code. DeepSeek's 1017 model card reports 42.3 for its Flash checkpoint, also using Claude Code. The Terminal-Bench leaderboard, checked separately, lists Astra and Fable as follows:
| Model | Published Terminal-Bench 4.0 result | Source and setup |
|---|---|---|
| Kimi K3.1 Pro | 65.7% | Moonshot launch evaluation, Claude Code |
| DeepSeek V4.1 Flash 1017 | 42.3% | DeepSeek model card, Claude Code |
| GPT-6 Astra | 58.2% ± 2.8% | Benchmark leaderboard, Codex at max effort |
| Claude Fable 5.1 | 57.9% ± 3.8% | Benchmark leaderboard, Claude Code at max effort |
The uncertainty ranges in the last two rows are the leaderboard's reported 95% confidence intervals. Do not attach them to the vendor-only rows or interpret the table as a matched four-model test.
The 0.3-point difference between Astra and Fable is particularly weak evidence for choosing one over the other: their reported intervals overlap, and their agents differ. Kimi's larger reported score makes it worth evaluating, but its launch footnotes combine vendor tests and external leaderboard results. They also disclose different tools and grading choices across some knowledge-work evaluations.
For your own coding trial, hold the repository, tools, task budget, and acceptance checks constant. For document work, inspect whether citations support each claim and whether spreadsheet formulas remain correct. A terminal benchmark cannot answer those questions for you.
Official API prices, including cache reads
The following are direct-provider standard token rates in USD per million tokens, checked October 29. They exclude taxes, paid tools, negotiated discounts, and gateway-specific prices.
| Model | Uncached input | Output | Cached input |
|---|---|---|---|
| Kimi K3.1 Pro | $2.00 | $10.00 | $0.20 |
| Kimi K3.1 Pro Highspeed | $4.00 | $20.00 | $0.20 |
| DeepSeek V4.1 Flash, off-peak | $0.15 | $0.60 | $0.003 |
| DeepSeek V4.1 Flash, peak | $0.30 | $1.20 | $0.006 |
| Claude Fable 5.1 | $10.00 | $50.00 | $0.25 |
| GPT-6 Astra | $10.00 | $50.00 | $1.00 |
Sources: Kimi pricing, DeepSeek pricing, Anthropic pricing, and OpenAI's Astra model page.
Three qualifications matter when applying these numbers:
- DeepSeek has scheduled rates. Peak hours are 01:00–04:00 and 06:00–10:00 UTC on weekdays, excluding Chinese public holidays. Other hours are off-peak. A queued batch may have different economics from a daytime interactive assistant.
- Astra has a long-input threshold. Above 272K input tokens, OpenAI documents 2× input and cache rates and 1.5× output rates for the full request. The base row is not a flat price across its entire context window.
- Cache writes are a separate billing category. Kimi lists $2/$4 for five-minute/one-hour writes on standard Pro; Fable lists $12.50/$20. Astra lists $12.50 for writes at the base tier. Do not cost a newly cached prefix at the read rate.
Kimi's cache guide adds a useful accounting detail: in its Chat Completions and Responses usage, cached reads, cache writes, and the uncached remainder are mutually exclusive parts of total input. Charging all input at the uncached rate and then adding every cache-write token again would double-count that portion.
A worked example shows where the price gap shrinks
First consider a base-rate subtotal for 100,000 uncached input tokens and 20,000 output tokens. This illustration excludes cache-write events, tools, retries, and long-input surcharges:
| Model | Token subtotal for the example |
|---|---|
| DeepSeek V4.1 Flash | $0.027 off-peak / $0.054 peak |
| Kimi K3.1 Pro | $0.40 |
| Kimi K3.1 Pro Highspeed | $0.80 |
| Claude Fable 5.1 | $2.00 |
| GPT-6 Astra | $2.00 |
Under identical token counts, Kimi's standard subtotal is 80% below the two premium rows. That is a price calculation, not a claim of 80% savings for an identical real-world job. Models tokenize inputs differently and can consume very different amounts of reasoning and output.
Now consider a follow-up request with 100,000 tokens already cached, 10,000 uncached input tokens, and 20,000 output tokens. Excluding the earlier cache creation and any new writes:
| Model | Follow-up token subtotal |
|---|---|
| DeepSeek V4.1 Flash | $0.0138 off-peak / $0.0276 peak |
| Kimi K3.1 Pro | $0.24 |
| Claude Fable 5.1 | $1.125 |
| GPT-6 Astra | $1.20 |
The cached 100K prefix alone costs $0.020 on Kimi and $0.025 on Fable: a half-cent difference. Most of the remaining gap in this example comes from output and fresh input. That is why an input-price discount should never be applied blindly to the whole bill.
For a fair production comparison, replay the same tasks, collect each route's actual usage categories, and include all attempts. The metric to optimize is:
cost per accepted task = total charges across all attempts / number of accepted tasks
Track reviewer time alongside that figure when human repair is a material part of delivery.
Reasoning and tool behavior can change the recommendation
Kimi's Chat Completions API supports low, high, and max reasoning effort, defaulting to max. Thinking is always enabled. An application that expects a generic medium setting or a non-thinking toggle needs a model-specific mapping.
The same reference says K3-series output defaults to 131,072 tokens and can be configured higher within the shared context. If input plus the requested generation budget exceeds the window, the API reduces the generation allowance. Long input can therefore constrain the result even when the configured output number appears generous.
DeepSeek documents both thinking and non-thinking modes, with thinking as the default, plus vision on the Flash endpoint. OpenAI's Astra page documents text and image inputs, text output, and reasoning levels from low through max. A video-processing workflow should not assume that a shared API format gives every model the same input modalities.
Keep the complete tool conversation when switching between requests. Validate structured output against your schema, preserve the model-specific state required by the endpoint, and capture the final usage breakdown when streaming. Those details affect both success rate and whether the cost comparison is accurate.
A routing policy a small team can actually operate
Start with a few named workload classes rather than a complicated automatic ranking:
| Workload | First candidate to test | Evidence needed before using it routinely |
|---|---|---|
| Repetitive extraction or bounded code edits | DeepSeek V4.1 Flash | Valid output, low repair rate, acceptable peak-hour cost |
| Difficult repository changes or visual research | Kimi K3.1 Pro | Accepted changes or grounded findings at a lower total cost |
| Established Claude-based professional workflow | Fable 5.1 | Completion quality, cache reuse, and reviewer effort |
| Complex engineering using OpenAI's tools | GPT-6 Astra | Task success sufficient to justify the route's total charges |
These are trial starting points, not measured winners. Use an explicit escalation trigger: repeated test failure, invalid tool arguments, or inability to support a required answer with evidence. A failed cheap attempt remains part of the task's total cost after escalation.
Before connecting a client, check the RouteKey model catalog for the current route, supported endpoint, and account pricing. Use the agent integration guide for configuration. An upstream launch announcement does not guarantee that every model, tool, or Highspeed variant is available on your chosen route.
FAQ
Is Kimi K3.1 Pro cheaper than Fable 5.1 and Astra?
Its standard uncached input and output rates are 80% lower in the checked direct-provider listings. Cache-read rates, output length, retries, tools, and accepted-task rates determine the actual savings.
Are Kimi K3.1 Pro weights already available?
The launch announcement schedules full weights for release by November 4, 2026. As of this article's October 29 check, hosted API availability and the planned weight release should be treated separately.
Does deepseek-v4-flash pin the older model?
No. DeepSeek's current documentation says that legacy name is accepted but served by V4.1-Flash-1017. Record the documented checkpoint and evaluation date as well as the string your client sends.
Are API prices the same as a coding subscription?
No. Kimi's documentation explicitly distinguishes token-billed API inference from Kimi membership and Kimi Code products. Apply the pricing rules for the actual credential and endpoint you use.
Is the highest benchmark score the right default model?
Only if it also meets your task, latency, and cost requirements. Test the full workflow, including failure recovery and human review, before making it the default route.
Sources and further reading
- Moonshot AI, Kimi K3.1 Pro launch and benchmark methodology, API reference, pricing, and cache accounting.
- DeepSeek, V4.1 Flash 1017 model card, release update, and model IDs, prices, and peak hours.
- Anthropic, model IDs and capabilities and Claude pricing and cache-write rules.
- OpenAI, GPT-6 Astra model specification and pricing.
- Terminal-Bench, benchmark leaderboard and confidence intervals.
- CostRouter, Kimi K3.1 Pro comparison, October 26, 2026; background reading, with the figures above checked against provider and benchmark sources.
- RouteKey, Qwen3.8 Flash vs GLM-5.3 Flash and Gemini 4 Argon migration guide.