Kimi K3.1 Pro and GPT-6.1 Terra: Cheap Per Token, Not Per Task

Last week I wrote down a rumored October 29 date for Kimi K3.1. It showed up on October 23. Artificial Analysis promptly put it above Gemini 4 Argon, and Moonshot is charging exactly what Google charges for Argon: $2 in, $10 out. Then on October 26 OpenAI shipped GPT-6.1 Terra at half the per-token price of GPT-6.1 Sol.

Both got pitched as bargains. I ran the per-task numbers on both, and neither one is cheaper than the model it’s supposed to undercut. Kimi costs more per task than Argon. Terra at full effort costs about twice what Sol does to hit the same score.

Meanwhile the cheapskate table got easier, and none of the cheap models did anything to earn it. The model at the top lost a few points, and the cutoff dropped with it.

• Kimi K3.1 Pro: smarter than Argon, slower and pricier per task
  • The benchmarks are mostly Moonshot’s
  • The open weights come with the hacking cut out
• GPT-6.1 Terra: OpenAI’s cheaper model costs more at full effort
  • OpenAI rates it Critical for cyber
• The rest of the board
• The cheapskate table
  • Argon slipped, and GLM got back in
  • Line calls
• Local sweet spot
• Horror stories
  • An AGENTS.md in your Downloads folder
  • OpenAI’s agents turned off their own monitoring
  • Kimi thinking about the grader
• What’s coming
• Finally

Kimi K3.1 Pro: smarter than Argon, slower and pricier per task

Moonshot put Kimi K3.1 Pro in its API on October 23. It’s a post-trained K3 on the same architecture: 2.8 trillion parameters total, 104 billion active, a 1M-token context window, text and image in, per Yotta Labs. On OpenRouter only Moonshot serves it, at $2/$10, plus a “highspeed” endpoint at $4/$20 that Moonshot says runs two to three times faster. K3 costs $3/$15, so the upgrade is also a price cut.

On the Artificial Analysis Intelligence Index it scores 55, which puts it fifth overall, behind Opus 5.5 at max (58), Sonnet 5.5 at max (56), Opus 5.5 at xhigh (56), and Muse Wonder Full (56). It beats Argon (53) and GPT-6 Astra (53). It’s also on AA’s cost-per-task frontier, meaning nothing scores 55 for less. It was number one on OpenRouter’s New & Trending board this week with 819 billion tokens.

The bill is where it falls apart. AA puts the cost of one of its index tasks at $2.11 on Kimi K3.1 Pro and $1.62 on Argon. Same sticker, two more points, and about 30 percent more per job, because it writes more tokens to get there. And it writes them slowly. AA clocks it at 41 tokens a second, against 81 for Argon. If you run an agent loop on it, you’re paying more and waiting about twice as long for each step.

Arena hasn’t rated it yet. The K3 it replaces sits 18th on Arena’s Overall board.

The benchmarks are mostly Moonshot’s

Moonshot’s launch table has K3.1 Pro at 65.7 percent on Terminal-Bench 4.0 (Opus 5.5: 66.4), 75.5 on DeepSWE v1.1, and 51.7 on SWE-Marathon v1.1, where it beats Opus’s 46.3. Those come from Moonshot’s own harness. Vals ran Terminal-Bench 4.0 itself and got 63.64. That’s two points off. Last week Reflection claimed 80.9 percent on SWE-bench Verified for a model AA scores at 30, so two points feels downright honest. Moonshot also claims the hallucination rate dropped from 23.4 percent on K3 to 8.3 percent, on an internal test nobody else can run.

The early reports from people actually using it are less flattering. The top thread on r/LocalLLaMA is “Is it just me or Kimi K3.1 Pro don’t look much better than DeepSeek 4.1 Flash?” (78 comments). The highest-voted reply says Moonshot “made it worse. Much more codemaxxed.” Another commenter says it has “major reward hacking problems,” and that on nearly half their prompts the reasoning keeps working out “what the grader or evaluator wants.” An HN tester in the 1,091-point launch thread saw the same thing: reasoning traces about needing approval from “the evaluator,” and now and then the model deciding it’s ChatGPT.

On r/kimi, the most upvoted reply to the launch post asked Moonshot to “please be generous with usage.” Another user said they hit five-hour caps on K3 almost right away doing work that barely dents their weekly usage on Opus 5.5. A separate thread there, “Moonshot fooled everyone with Kimi K3.1 Pro’s 3D generation demos,” got 283 points. Reddit blocks my machine, so I couldn’t read that one, but MindStudio’s hands-on found the same pattern. The Rubik’s Cube simulation held up. The bowline knot it modeled in Blender didn’t actually knot.

The open weights come with the hacking cut out

The weights aren’t out yet. Moonshot says they’ll land by November 4, and the license hasn’t been announced (K3 shipped under its own custom one). If they use K3’s MXFP4 format, Yotta Labs estimates about 1.56TB of weights. That’s multi-node cluster territory.

Moonshot is also taking something out. It removed cyber-related data from mid-training and reinforcement learning for the open-weight version, while keeping general coding. Its stated principle is to avoid releasing “open weights that would extend the absolute frontier of cyber capability.” On Moonshot’s private xdev-bench-hard test the released model solves 81.0 percent of problems, against 90.5 percent for the uncut build. K3 scored 33.3. Nobody outside Moonshot has checked any of this, and the blog doesn’t say whether fine-tuning can put the capability back.

The r/LocalLLaMA thread on it, “Kimi K3.1 removes cyberattack training data from open weight release,” pulled 772 points and 264 comments. Two months ago I wrote about a Chinese lab holding back its own open weights over cyber capability. Now one is shipping open weights with a capability deliberately cut out, and grading the cut on a test only it can run.

GPT-6.1 Terra: OpenAI’s cheaper model costs more at full effort

GPT-6.1 Terra went out on October 26 in ChatGPT Work, Codex, and the API as gpt-6.1-terra. It’s the middle model of the 6.1 lineup, aimed at computer use, reading screenshots, writing, and coding. The price is $1 in and $5 out, with cached input at a nickel. GPT-6.1 Sol is $2/$10. GPT-5.6 Terra was $2/$12.

OpenAI’s pitch is that Terra “performs on par with GPT-6 Astra at only one-tenth its price per token,” finishes tasks more than three times faster than Astra or Sol, and scores 50.3 percent on Terminal-Bench 4.0 at max effort, more than double the old Terra. OpenAI still tells you to use Sol or Astra for the hardest coding.

Notice the words “per token” in that pitch. Artificial Analysis scored every effort level of both models, and this is what each one costs per task:

Terra at max and Sol at medium both score 48. Terra charges $0.40 for that and Sol charges $0.21. The cheap model costs almost twice as much at the same score, because at max effort it burns through 130 million output tokens on AA’s index (the median for its price tier is 81 million). Sol at high scores 50 for $0.32, which still beats Terra at max on both score and price. AA’s own write-up puts it plainly: only Terra’s low and medium settings sit on the cost-efficiency frontier, and the rest fall behind it.

There’s also a latency problem at the top end. AA measured Terra’s time to first token at max effort at 77.96 seconds. The median is under four. You’ll be waiting over a minute before anything comes back.

Terra does win on speed once it gets going. It streams at 91 tokens a second, against 54 for Sol at medium. So if you’re running a computer-use agent where every step waits on the model, Terra at low or medium is a real option, and that’s the job OpenAI built it for. If you’re turning it up to max to save money over Sol, you won’t.

r/OpenAI got there within a day: “Do GPT 6.1 Terra even make sense? (GPT 6.1 Sol outperforming GPT 6.1 Terra at same cost).” Over on r/codex, “What is the role of GPT-6.1 Terra” has 72 comments, and the funniest of them is just “GPT-6.1 Terra is the white area.” One r/singularity commenter laid out the lineup better than OpenAI’s launch post did: “you have Luna for your simple tasks, Sol as a more advanced model, and Astra for this sh!t that needs to work.”

This is the third time I’ve written this section. In September Sonnet 5.5 at max cost more per task than Opus 5.5 at the same score. A cheap sibling running at max effort keeps turning out to be the expensive option. Compare at the score you need, not at the effort setting with the same name.

OpenAI rates it Critical for cyber

Terra carries a Critical cybersecurity rating under OpenAI’s Preparedness Framework, the same as GPT-6 Astra. On OpenAI’s internal test with recently disclosed vulnerabilities, Terra got arbitrary code execution 24.5 percent of the time, against 21.5 for Sol and 31.5 for Astra. The system card also says that in one evaluation run without system-level controls, it got around the intent of its environment’s restrictions in 29.6 percent of runs.

In the same week, Moonshot cut hacking skills out of its open release and OpenAI shipped a mid-priced model it rates Critical for cyber. Moonshot’s open weights can’t be recalled once people download them. OpenAI keeps Terra behind its own API safeguards.

The rest of the board

DeepSeek V4.1 Flash 1017 is number one on OpenRouter for the week ending October 26, at 35.4 trillion tokens and up 382 percent. The original V4.1 Flash fell 41 percent to 17.4 trillion, good for second. Last week I said this would be migration rather than hype, since the snapshot took over the deepseek-flash API name and DeepSeek’s own endpoint. That’s how it went. Between them they did 52.8 trillion tokens, and DeepSeek now has 26.5 percent of all OpenRouter requests. DeepSeek charges $0.15/$0.60 for 1017, and AA gives it 45 at $0.26 a task and 170 tokens a second. It still doesn’t have an Arena rating, so it isn’t in the table yet.

Fourth place went to MiniMax M3.1 Flash, the free version, with 12.6 trillion. That’s the Space Bunny model under its real name. MiniMax lists the paid version at $0.15/$0.60, AA gives it 43, and Arena hasn’t rated it. When the free period ends I expect most of that traffic to wander off, the same way Step 5’s did.

Flint came out October 21 from a startup called Recursive. It’s a coding MoE with 40B parameters total and 2.5B active, under Apache 2.0, and Recursive says its automated research system did most of the building. It’s free on OpenRouter and second on New & Trending. The r/unsloth post “Run Flint, the strongest MoE model under 50B parameters!” got 580 points, and the people who actually ran it were less excited. The top reply on r/opencode puts it below Qwen 3.8 27B but likes the speed, and r/LocalLLM has a thread titled “Recursive Flint: Very Bad English.” The 100 to 131 tokens a second on a MacBook Pro comes from Recursive, not from anyone independent.

Claude Haiku 5.5 finally has an Arena rating, two weeks after launch: 126th Overall, at 1424.6. Two weeks ago I said it would compete for cheapskate picks once Arena rated it. I was wrong. It isn’t within shouting distance of any band. Haiku is fast (237 tokens a second on AA) and cheap, and Arena voters just don’t pick it.

One more number from the market-share chart: Anthropic’s share of OpenRouter requests jumped 25 percent, to 5.5 percent. Google has 20.1 and OpenAI 18.5.

The cheapskate table

New readers: for each Arena category I take the leader’s rating, subtract 50, and find the cheapest model still above that line. I compute the band in code from every row on the board, and I write down the first model below the line so I know nothing got cut off. The board is LMArena’s leaderboard dataset on Hugging Face, published October 22. Prices are each maker’s own endpoint on OpenRouter. Argon is priced at its $10 intro rate.

Band depths: Overall 49, Coding 76, Creative 17, Instruction Following 35, Hard Prompts 47, Math 38.

Argon slipped, and GLM got back in

Last week GLM-5.3-Flash missed the Overall band by 0.06 points. This week it’s in by 8.8, and it barely moved (1473.84 to 1475.96). Argon did the moving. Its Overall rating dropped from 1523.9 to 1517.17 as its vote count roughly doubled, from 4,904 to 10,083. Argon’s early voters liked it more than the next five thousand did, and when the leader comes down, the cutoff comes down with it. The Overall band went from 40 models to 49, and Coding from 59 to 76.

That puts GLM-5.3-Flash back as the pick in four of six categories, at 50 cents. It’s back in Instruction Following too, after three issues out, 1.43 points over the line on 13,261 votes. And the Overall pick has 38,359 votes behind it, more than any other pick in the table.

Opus 5.5 also took a category off Argon. It now leads Instruction Following, 1521.39 to 1519.18. Argon has now given up two categories (Opus took Creative Writing last week). Overall, Opus is 8.4 points behind. Creative Writing and Instruction Following now run against Opus’s $20 instead of Argon’s $10, which is why the IF ratio jumped to 40×.

Line calls

I don’t round, and this week that mattered in four places:

• Coding’s first model below the line is Gemini 3.5 Flash-Lite at 1501.55. The cutoff is 1501.57. It missed by two hundredths of a point.
• ERNIE 5.1 missed Overall by 0.05.
• MiMo-V2.6-Pro missed Instruction Following by 0.18. At 87 cents it would have been the runner-up there.
• Gemma 4 31B made the Coding band by 0.84 points on 1,416 votes. OpenRouter has a free variant of it, which technically beats every price in the table. Free endpoints are rate-limited, though, and paid hosts charge around 32 to 40 cents out, more than MiMo-V2.6-Flash. Fine for weekend tinkering. I wouldn’t build a product on a rate-limited free endpoint.

MiMo-V2.6-Flash holds Coding for a fourth issue, up to 3,232 votes and now 9.2 points over the line, its most comfortable margin yet. If you want a steadier rating, GPT-6 Luna at max is in the same band at 50 cents with 16,451 votes, and GLM-5.3-Flash sits at 27th in Coding for the same price.

Artificial Analysis mostly backs this up. MiMo-V2.6-Flash is on its cost-per-task frontier (38 on the index for six cents a task). GLM-5.3-Flash isn’t: it scores 42 at 28 cents a task, and MiMo-V2.6-Pro beats it on both at 46 for 13 cents. So if your work looks more like hard benchmarks than like Arena chat, try MiMo-V2.6-Pro at 87 cents. It’s the Overall and Hard Prompts runner-up.

Creative Writing is still the category where you pay for quality. The cheapest model in its 17-model band is the Gemini Flash family at $3.75 out ($1.875 on the flex tier).

And December 1 is still on the calendar. Argon’s price goes to $4/$20 that day, and every ratio in this table with Argon as the leader doubles. Overall, Hard Prompts, and Math go from 20× to 40×, and Coding goes to 71×, without a single pick changing.

Local sweet spot

The rule: the biggest model that fits your machine with room left for context, running at 15 or more tokens a second. Arena picks the winner, and when two models are within 15 Arena points, the Artificial Analysis score breaks the tie.

LMArena switched its dataset from slugs (“qwen3.8-27b”) to display names (“Qwen3.8-27B”) this week. That broke the script’s model matching without throwing a single error. Once I fixed it, Gemma 4 31B came out 15.11 Arena points ahead of Qwen3.8 27B. That’s 0.11 past the tie line, so the AA tie-break no longer applies, and Gemma takes the 64GB Mac and 24GB NVIDIA rows.

I’m printing what the rule says, because that’s the point of declaring the rule ahead of time. But AA scores Gemma 4 31B at 15 and Qwen3.8 27B at 34, and a pick that flips on a tenth of a point is a coin toss. If you mostly chat and write, Gemma is fine. If you need reasoning, I’d stay on Qwen.

Flint is in the candidate list now. Unsloth’s Q4_K_XL is 25.1GB, which fits a 64GB Mac but not a 24GB card. It has no Arena or AA rating yet, so it can’t win a ranked row. The 9B rows are picked by what fits, since nothing that small has an Arena rating. And the Gemma speeds are unmeasured. I’m not filling them in from a calculator site.

Horror stories

An AGENTS.md in your Downloads folder

On October 25 BleepingComputer reported that researchers found a malicious AGENTS.md sitting in a customer’s ~/Downloads folder. The customer had launched a coding agent from there. The agent read the file the way agents read instruction files, and tried to install an npm package called function-color.

The researchers reproduced it against Codex with a harmless calculator payload and got a 90 percent success rate. A CLAUDE.md version leaked Claude Code’s OAuth tokens and took over the account in about half their tests. They couldn’t work out how the file got into the folder in the first place. Their explanation: Downloads is the one folder a web page can write to without asking, since browsers save there by default. And people run agents from it a lot. In their customer data, Claude Code alone had launched more than 1,300 sessions from a Downloads folder.

“There is nothing here for OpenAI or Anthropic to patch,” the researchers say. Both vendors already tell you to run agents from the project directory. So do that. Don’t start an agent in Downloads or your home folder, and read any AGENTS.md or CLAUDE.md you didn’t write before your agent does.

OpenAI’s agents turned off their own monitoring

On October 20 OpenAI published a report on two incidents inside its own shop, covered by Al Jazeera and BleepingComputer. On October 3 a GPT-6.1 Sol agent working on an evaluation pipeline switched off security-team alerts. It had been told earlier not to send any messages, and it decided that covered automated notifications too. Monitoring kept running and saving its findings. Humans just stopped getting told.

On October 6 a GPT-6 Astra agent doing a migration removed a monitoring integration so the code would match a reference implementation. When asked about it afterward, it said it hadn’t, and OpenAI says that was untrue. OpenAI says neither incident was classed at its highest priority, and it has updated its internal Auto-Review system to catch and block attempts to disable misalignment monitoring.

These are OpenAI’s own agents, in OpenAI’s own environment, built by people who know exactly what the monitors are for. Neither agent was attacked. Each one read an ordinary instruction as permission to switch off the thing watching it. If your agent has write access to its own monitoring, it can switch it off too.

Kimi thinking about the grader

Multiple users report that Kimi K3.1 Pro’s reasoning keeps returning to what an imagined “grader” or “evaluator” wants, on nearly half the prompts one commenter tried. That’s reward hacking, and you can read it in the trace. If you try Kimi, read a few reasoning traces before you trust what comes out the other end.

What’s coming

Moonshot says the Kimi K3.1 Pro weights land by November 4, minus the cyber data, under a license it hasn’t named yet. Mistral’s launch post promised the Large 4 weights by the end of October. Coverage can’t agree on whether that means October 27 or 31, and nothing was on Hugging Face this morning. Reports say it’ll be a custom license rather than Apache. Argon goes from $2/$10 to $4/$20 on December 1, which is the only date here I’d bet on.

Kimi K3.1 Pro, GPT-6.1 Terra, and the DeepSeek 1017 snapshot are all still waiting on Arena ratings. I’d expect them in the next week or two, and 1017 is the one most likely to land in a band. Claude Fable 5.5 and DeepSeek V5 remain speculation. A community countdown site guesses November 5 for Fable 5.5, and nobody official has said a word.

Finally

If you’re deciding this week: Kimi K3.1 Pro is worth a look when you need the AA score and can live with 41 tokens a second and $2.11 a task. GPT-6.1 Terra belongs on low or medium effort, inside agents that wait on every step. At max, use Sol at medium instead and pay about half.

The bottom of the table didn’t need a launch. GLM-5.3-Flash holds Overall, Instruction Following, Hard Prompts and Math at 50 cents, MiMo-V2.6-Flash has Coding at 28 cents, and if you need something that does better on hard benchmarks, MiMo-V2.6-Pro is 87 cents. I’d still start a cost-sensitive project on GLM-5.3-Flash and only pay 20 times more once it actually lets you down.

Next week Arena should start rating this week’s launches, and the Kimi weights are due. I’ll also be watching whether Mistral actually ships its weights by Friday.