Kimi K3 vs GPT-6 Astra: Pricing, Benchmarks and When to Switch
September 25, 2026 · kimi k3 vs gpt-6 astra · gpt-6 astra · gpt-6 astra pricing · gpt-6 astra benchmarks · kimi k3 vs gpt-6 · kimi k3 api pricing · kimi k3 vs chatgpt · openai vs moonshot
GPT-6 Astra is the stronger model, and Kimi K3 is the much cheaper one. On list price, K3 costs $3 input / $15 output per million tokens against Astra’s $10 / $50, and Astra doubles to $20 / $75 once a prompt crosses 272K tokens. On independent testing, Astra scores 53 vs K3’s 44 on the Artificial Analysis Intelligence Index. Because K3 writes more tokens per task, its real per-task saving is about 40%, not 70%.
This comparison is current as of September 25, 2026. It uses OpenAI’s own model and pricing pages, Moonshot’s pricing docs, AWS and Azure listings, and independent scores from Artificial Analysis. Where a number is vendor-reported rather than independently measured, the article says so. If you want the previous round, read the Kimi K3 vs GPT-5.6 comparison first; much of the landscape has moved since July.
Kimi K3 vs GPT-6 Astra at a glance
| Kimi K3 | GPT-6 Astra | |
|---|---|---|
| Maker | Moonshot AI | OpenAI |
| Released | July 16, 2026 | September 3, 2026 (staged) |
| Architecture | 2.8T-parameter MoE, 104B active | Undisclosed |
| Weights | Open, Kimi K3 License | Closed |
| Context window | 1,048,576 tokens | 1,050,000 (922K max input, 128K max output) |
| Input modalities | Text, image | Text, image |
| Reasoning levels | low / high / max | low / medium / high / xhigh / max |
| Input / cached / output (per 1M) | $3 / $0.30 / $15 | $10 / $1 / $50 |
| Over 272K input | Same price | $20 / $2 / $75 for the full request |
| AA Intelligence Index (max) | 44 | 53 |
| Output speed (AA) | 35 tok/s | 52 tok/s |
| Cost per AA index task | $2.00 | $3.26 |
| Self-hosting | Yes (~1.56TB of weights) | No |
Sources: OpenAI’s GPT-6 Astra model page, Moonshot’s K3 pricing page, and the Artificial Analysis Astra vs K3 comparison.
What GPT-6 Astra actually is
OpenAI launched GPT-6 Astra on September 3, 2026, calling it its most intelligent model. The rollout was staged. It started with enterprise customers in OpenAI’s gated Daybreak program, then expanded over the following days to ChatGPT Plus, Pro, Business and Enterprise, the API, and the big clouds (VentureBeat). Amazon put Astra on Bedrock on September 8, and Microsoft lists it in Foundry across Global regions and US/EU Data Zones.
The staged launch had a specific cause. Astra is the first OpenAI model rated Critical for cybersecurity under its Preparedness Framework. VentureBeat describes it as able to find previously unknown vulnerabilities and chain exploits with little human guidance. OpenAI’s response was to keep the strongest cyber capabilities for vetted defenders in a “Daybreak Blue” track, with tighter restrictions and monitoring for everyone else. That matters if your workload touches security tooling, because stricter guardrails mean more refusals.
The API facts, from OpenAI’s model page:
- Model ID:
gpt-6-astra(one snapshot so far) - Context: 1,050,000 tokens total, with at most 922,000 input and 128,000 output tokens
- Knowledge cutoff: April 30, 2026
- Endpoints: Responses, Chat Completions and Batch. Fine-tuning, Realtime and Assistants are not supported
- Tools: web search, file search, code interpreter, hosted shell, apply patch, computer use, MCP and tool search
Astra did not stay at the top of OpenAI’s price list for long. On September 22, OpenAI released GPT-6 Sol and Luna, cheaper models “cut from the same cloth as Astra” according to TechCrunch. GPT-6 Sol lists at $2 / $10, which is below K3’s price. Most K3-vs-Astra coverage misses this, and it changes the switching question considerably (more below).
Pricing, line by line
Here is every rate that matters, per million tokens, standard tier:
| Rate | Kimi K3 (Moonshot) | GPT-6 Astra (≤272K) | GPT-6 Astra (>272K) | GPT-6 Sol (≤272K) |
|---|---|---|---|---|
| Input | $3.00 | $10.00 | $20.00 | $2.00 |
| Cached input | $0.30 | $1.00 | $2.00 | $0.20 |
| Cache write | $3.00 (5-min TTL) / $6.00 (1-hour) | $12.50 | $25.00 | $2.50 |
| Output | $15.00 | $50.00 | $75.00 | $10.00 |
| Batch / Flex | Not offered by Moonshot | 50% off | 50% off | 50% off |
| Fast mode | Not offered by Moonshot | 2x price | 2x price | 2x price |
Sources: OpenAI API pricing, Moonshot K3 pricing. Checked September 25, 2026.
Three pricing details matter more than the headline numbers:
1. Astra’s 272K rule reprices the whole call. OpenAI’s wording is exact: “Prompts with more than 272K input tokens are priced at 2x input and cache rates and 1.5x output for the full request.” A 273K-token prompt is not billed at $10 for the first 272K and $20 for the last thousand. The entire call moves to $20 / $2 / $75. For long-document agents, this is the biggest single cost difference between the two models.
2. Cache writes are close to free on K3 and a surcharge on Astra. OpenAI bills cache writes at 1.25x the uncached input rate. Moonshot now publishes a cache-write line too, but its default 5-minute TTL is priced at $3.00, the same as a normal input token, so there’s no premium unless you choose the 1-hour TTL at $6.00. After that, cached reads cost $0.30 on K3 and $1.00 on Astra.
3. Astra has discount tiers and K3’s official API doesn’t. OpenAI’s Batch and Flex tiers cut Astra to $5 / $25, which narrows the gap for offline jobs. Moonshot’s own API doesn’t offer a batch tier, but K3 on Amazon Bedrock supports a Flex tier at 0.5x ($1.50 / $7.50) and Priority at 1.75x.
Regional processing adds cost on both sides. OpenAI charges a 10% uplift for data-residency endpoints on models released after March 5, 2026. Azure prices Astra at $11 / $55 in the US Data Zone and $12 / $60 in the EU Data Zone. On Bedrock, K3’s US-only inference profile costs $3.30 / $16.50, also a 10% premium.
Worked cost examples
Five realistic calls, calculated from the list prices above (standard tier, before any batch discount):
| Scenario | Kimi K3 | GPT-6 Astra | GPT-6 Sol |
|---|---|---|---|
| Chat turn: 20K in, 2K out, no cache | $0.09 | $0.30 | $0.06 |
| Agent step: 150K cached + 10K fresh in, 4K out | $0.135 | $0.45 | $0.09 |
| Long doc: 400K in (uncached), 20K out | $1.50 | $9.50 | $1.90 |
| Long doc, cached: 350K cached + 50K fresh, 20K out | $0.555 | $3.20 | $0.64 |
| Monthly: 2B input (90% cached), 100M output | $2,640 | $8,800 | $1,760 |
The long-document row: K3 is 0.4M × $3 + 0.02M × $15 = $1.50. Astra crosses 272K, so the full request uses long-context rates: 0.4M × $20 + 0.02M × $75 = $9.50, or 6.3x more (versus about 3.3x for short prompts). The agent-step row ignores cache-write premiums, which would add about $0.025 per step on Astra and nothing on K3 at the default TTL.
The monthly line assumes identical token counts on every model. In practice that doesn’t hold.
Cost per task: where the price gap shrinks
Token prices don’t tell you what a finished job costs, because models spend very different numbers of tokens on the same problem. Artificial Analysis publishes this directly. When it runs its full Intelligence Index, it records how many tokens each model generates and what the run costs:
| Artificial Analysis measurement | Kimi K3 (max) | GPT-6 Astra (max) | GPT-6 Sol (high) |
|---|---|---|---|
| Intelligence Index | 44 | 53 | 43 |
| Output tokens per task | 48K | 27K | 10K |
| Cost per task | $2.00 | $3.26 | $0.37 |
| Cost to run full index | $3,658 | $5,324 | $610 |
| Output speed | 35 tok/s | 52 tok/s | 96 tok/s |
Sources: AA comparisons of Astra vs K3 and GPT-6 Sol vs K3.
K3 thinks at length. It generated about 1.8x as many output tokens per task as Astra, so its 70% per-token discount becomes a 39% per-task discount ($2.00 vs $3.26). That is still a real saving, and in exchange you get 9 fewer points on the index.
GPT-6 Sol changes the picture more. It matches K3 on the index (43 vs 44) at less than a fifth of the per-task cost, because it is both cheaper per token and far more concise. If your only reason for choosing K3 over OpenAI was price, compare it against Sol, not Astra.
One point in K3’s favour: in agentic coding, cache-hit rates above 90% move most input billing to the $0.30 tier. The AA index is a mix of tasks and doesn’t capture that. Your own traffic can shift these numbers, so measure before you commit.
Benchmarks: independent numbers first
Vendor launch charts are useful but not neutral, and OpenAI’s Astra charts didn’t include Kimi K3 at all. The cleanest head-to-head is Artificial Analysis, which runs both models through the same harness:
| Benchmark (Artificial Analysis) | Kimi K3 (max) | GPT-6 Astra (max) |
|---|---|---|
| Terminal-Bench 4.0 | 13% | 59% |
| AutomationBench-AA | 58% | 68% |
| Humanity’s Last Exam | 47% | 55% |
| CritPt (physics) | 23% | 32% |
| GDP.pdf | 22% | 31% |
| AA-Omniscience | 20 | 43 |
| GDPval-AA v2.1 (Elo) | 1,524 | 1,542 |
| AA-Briefcase v1.1 (Elo) | 1,505 | 1,569 |
| SciCode | 59% | 56% |
| AA-LCR v1.1 (long-context reasoning) | 89% | 81% |
Terminal-Bench 4.0 is K3’s weak spot. Moonshot’s launch materials showed K3 at 88.3 on Terminal-Bench 2.1, but version 4.0 is a newer, harder suite. Under AA’s harness, K3 scores 13% against Astra’s 59%. K3 was tuned and reported under Moonshot’s KimiCode harness, so some of the gap may come from harness mismatch. Even so, a difference this large is a real signal for shell-driven agents.
K3 wins on long-context reasoning. An 89% vs 81% result on AA-LCR fits with K3 being priced and built for its full 1M window. It is also the one area where Astra’s 272K surcharge hurts most. If your work involves reading very large documents, K3 is both more accurate on this test and much cheaper.
On the professional-work Elo tests (GDPval-AA, Briefcase), the gap is small: 18 and 64 points. The large Astra leads are on agentic terminal work, knowledge accuracy (Omniscience) and hard science.
Vendor-reported numbers (read with care)
Some benchmarks appear in both vendors’ launch materials. These are self-reported, use different harnesses, and aren’t strictly comparable:
| Benchmark | Kimi K3 (Moonshot) | GPT-6 Astra (OpenAI) |
|---|---|---|
| BrowseComp | 91.2 | 91.5 |
| GPQA Diamond | 93.5 | 96.0 |
| Humanity’s Last Exam (with tools) | 56.0 | 57.2 |
| DeepSWE | 67.5 | 74.1 (v1.1) |
K3 figures: the Hugging Face model card. Astra figures: OpenAI’s launch post as compiled by Vellum. Browsing and tool-assisted HLE are effectively tied; Astra leads GPQA and DeepSWE. OpenAI also reports Astra results with no K3 counterpart, including 72.6% on OSWorld 2.0 and 97.6% on FrontierMath Tier 4.
Speed and latency
Astra streams faster, at 52 vs 35 output tokens/s on AA, but at max effort it reasons much longer before answering: about 364 seconds to its first answer token versus about 61 seconds for K3, and 374s vs 75s end-to-end for a 500-token reply. For interactive chat at max effort, K3 feels faster. For long agent runs, Astra’s throughput and leaner output matter more. For latency-sensitive use, drop both to a lower effort level.
Privacy, data residency and where each one runs
This area has changed the most since July. Both models are now on AWS. Kimi K3 became generally available on Amazon Bedrock on September 18, with a us.moonshotai.kimi-k3 profile that keeps requests inside US regions and a global profile at base price. 36Kr reports that K3 is also reachable through Microsoft Foundry with Fireworks providing the inference, while Google Cloud talks were still unresolved. The hyperscaler gap described in our provider guide has mostly closed.
| Question | Kimi K3 | GPT-6 Astra |
|---|---|---|
| Trains on your API data by default? | Depends on host; self-host = no data leaves | No (OpenAI data controls) |
| Default retention | Varies by provider | Up to 30 days for abuse monitoring |
| Zero data retention | Fireworks ZDR, self-hosting | Approved customers, eligible endpoints |
| US-only processing | Bedrock US profile, US providers | US data residency, Azure US Data Zone |
| EU processing | EU providers such as Nebius, self-host | EU data residency (+10%), Azure EU Data Zone |
| Can you run it on your own hardware? | Yes | No |
Moonshot is a Chinese company. Teams that won’t send data to its first-party API can still use K3 through AWS or US providers. OpenAI’s regional processing covers more regions (US, EEA plus Switzerland, UK, Canada, Japan, India, Singapore, South Korea, Australia, UAE), with non-US regions requiring approval for modified abuse monitoring.
Open weights vs closed: what it’s worth
K3’s weights are downloadable. Astra’s are not and won’t be. In practice, open weights give you:
- No lock-in. If a host reprices or degrades, you change endpoints and keep the model.
- Fine-tuning. Astra’s API has no fine-tuning endpoint. K3 can be tuned directly or through providers like Fireworks.
- Air-gapped deployment, for teams with serious hardware budgets. The weights are about 1.56TB and need about 1.7TB or more of GPU memory to serve (VRAM guide), so this is an option for companies, not individuals.
- Licence terms to check. The weights ship under the Kimi K3 License, not plain MIT. Read our licence breakdown before you resell K3 at scale.
Astra’s advantage is everything OpenAI builds around it: hosted computer use, shell and web tools in the Responses API, ChatGPT and Codex integration, and enterprise agreements your procurement team may already have.
Which should you pick?
| Your situation | Pick |
|---|---|
| Terminal-heavy or computer-use agents | GPT-6 Astra |
| Hard math, science, high-stakes answers | GPT-6 Astra |
| Reading or reasoning over 300K+ token documents | Kimi K3 (better AA-LCR score, no 272K surcharge) |
| Cached coding loops at volume | Kimi K3, or test GPT-6 Sol |
| Cheapest capable OpenAI option | GPT-6 Sol ($2/$10, K3-level index score) |
| Offline batch jobs | Astra Batch ($5/$25) or K3 Flex on Bedrock ($1.50/$7.50) |
| Must self-host or fine-tune | Kimi K3 (open weights) |
| Security research tooling | Test both. Astra’s Critical rating brings tighter guardrails |
| Already standardized on ChatGPT/Codex | Astra for hard tasks, Sol for the rest |
When switching from K3 to Astra pays off: when Astra’s higher success rate removes retries or human review that cost more than the roughly 1.6x per-task premium. Terminal agents are the clearest case, given the 59% vs 13% gap on Terminal-Bench 4.0.
When staying on K3 pays off: long-context workloads, cache-heavy pipelines, and anything that needs weights you control. If you’re choosing K3 only to save money, run GPT-6 Sol through the same evaluation first.
The sensible default is to route between models. Both are available on Bedrock and OpenAI-compatible APIs, so sending hard tasks to Astra and routine ones to K3 or Sol is a configuration change, not a migration.
Honest caveats
- Early numbers. Astra is three weeks old and Sol is three days old. AA scores and provider prices will move.
- Harness effects. K3’s Terminal-Bench 4.0 result under AA differs sharply from Moonshot’s own harness results on older versions. Test with your own tools.
- List price is not your bill. Cache-hit rate, output length and retries matter more than per-token price.
- Pricing changes. OpenAI’s list prices already move within a generation: GPT-5.6 Sol now lists at $4/$20, a rate coverage describes as promotional. Recheck both pricing pages before you commit budget.
FAQ
Is Kimi K3 cheaper than GPT-6 Astra? Yes, by a wide margin on list price. Kimi K3 costs $3 input, $0.30 cached input and $15 output per million tokens across its full 1M window. GPT-6 Astra costs $10, $1 and $50, rising to $20, $2 and $75 for prompts over 272K tokens. Per completed task the gap is smaller, because K3 writes more tokens: Artificial Analysis measured $2.00 per Intelligence Index task for K3 versus $3.26 for Astra at max effort.
Is GPT-6 Astra better than Kimi K3? On independent testing, yes. Artificial Analysis scores GPT-6 Astra (max) at 53 on its Intelligence Index versus 44 for Kimi K3 (max), and Astra leads on Terminal-Bench 4.0 (59% vs 13%), AutomationBench-AA and Humanity’s Last Exam. K3 wins on long-context reasoning (AA-LCR 89% vs 81%) and SciCode (59% vs 56%). Astra is the stronger model; K3 is the cheaper and more open one.
What is GPT-6 Astra’s 272K long-context pricing rule? OpenAI’s model page says prompts with more than 272K input tokens are priced at 2x input and cache rates and 1.5x output for the full request. That moves Astra from $10/$50 to $20/$75 per million tokens for the entire call, not just the tokens past 272K. Kimi K3 has no equivalent tier and bills $3/$15 across all 1,048,576 tokens.
Is GPT-6 Sol a better deal than Kimi K3? For many workloads, yes. GPT-6 Sol, released September 22, lists at $2 input and $10 output per million tokens, below K3’s $3/$15. Artificial Analysis scores Sol (high) at 43 versus K3 (max) at 44 on its Intelligence Index, with a cost per task of $0.37 versus $2.00. K3 keeps the advantages of open weights, self-hosting and a slightly larger usable context.
Can I use Kimi K3 and GPT-6 Astra on AWS Bedrock? Yes, both. GPT-6 Astra reached Amazon Bedrock on September 8, 2026 at OpenAI’s first-party rates. Kimi K3 became generally available on Bedrock on September 18, 2026 at $3/$15 via Global cross-Region inference, or $3.30/$16.50 through the US-only inference profile. Astra is also in Microsoft Foundry with US and EU Data Zone deployments.
Which is better for data privacy, Kimi K3 or GPT-6 Astra? It depends on the boundary you need. OpenAI does not train on API data by default, retains it up to 30 days for abuse monitoring, and offers Zero Data Retention and regional processing to approved customers. Kimi K3 can be run through US-hosted providers or AWS, or self-hosted from its open weights so data never leaves your own infrastructure. Astra can never be self-hosted.
Should I switch from Kimi K3 to GPT-6 Astra? Switch the tasks where K3 visibly fails: terminal-heavy agents, computer use, hard math and science, and work where a wrong answer is expensive. Keep K3 for cached coding loops, long-document reading, high-volume pipelines and anything that needs open weights or self-hosting. Route the two side by side and compare cost per successful task before moving everything.
The bottom line
GPT-6 Astra is now the stronger model by a clear margin. It scores 9 points higher on the independent AA index and leads by a wide gap on terminal agents. Kimi K3 is still the better value for long-context and cache-heavy work. On list price K3 is 3.3x cheaper, and 6.3x cheaper once Astra’s 272K rule applies. Per finished task, K3 is about 40% cheaper, because it uses more tokens. The less obvious finding is that OpenAI’s own GPT-6 Sol now matches K3’s index score at a fraction of the per-task cost. For teams that chose K3 on price alone, Sol is now the main competitor, more than Astra. Open weights, self-hosting and no long-context surcharge remain the reasons to stay on K3.
Keep reading: the predecessor Kimi K3 vs GPT-5.6 comparison, every Kimi K3 API provider, Kimi K3 vs Claude Fable 5.1, and the full Kimi K3 specifications.