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There is no defensible universal winner among Kimi K3.1 Pro, Claude, GPT, and Gemini. The best choice depends on the work: Kimi is especially interesting when you need a very large context, an API-priced model, coding or agentic workflows, or a data-analysis option with a published task-specific result. Gemini 3.1 Pro has documented multimodal input and broad model-card testing. Current Claude and GPT choices cannot be ranked here because no matched, current primary-source prices, versions, and test results were established for them.
Use the comparison below as a selection method rather than a leaderboard. Match the model to your inputs, tools, quality bar, latency target, deployment requirements, and actual token volume.
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The short answer: choose by workload, not by model name
Start with the constraints that can make a model unsuitable before looking at benchmark scores.
- Choose Kimi K3.1 Pro first for evaluation when your job involves very long material, code-heavy or agentic work, financial or analytical research, or API usage where its published token rates fit your budget. Moonshot describes it as a post-trained version of Kimi K3 and the first model in the K3.1 series.
- Choose Gemini 3.1 Pro first for evaluation when one request must combine text, images, audio, video, or an entire code repository and you want a dated public model card with results across several task families.
- Treat Claude and GPT as candidates to test, not as automatic winners. The evidence available for this article does not establish which current Claude or GPT version, price, or configuration is comparable to the two documented models above.
The practical answer is usually a shortlist of one or two models tested on your own prompts. A benchmark from a different harness, tool configuration, or attempt policy can identify an interesting candidate, but it cannot decide your production architecture by itself.
#1 Best Overall
What Kimi K3.1 Pro is and where it is available
Moonshot AI’s announcement calls Kimi K3.1 Pro “the first model in our new K3.1 series” and its most capable 3T-class open-weight model to date. It is described as a post-trained Kimi K3 model, with emphasis on coding, 3D creation, financial research, and agentic capabilities. The announcement says the model is available through Kimi.com, Kimi Work, Kimi Code, and the Kimi API: Moonshot’s Kimi K3.1 Pro announcement.
The same announcement says full model weights will be released by November 4. That is an announced release date, not proof that downloadable weights were already available on October 25, 2026. If self-hosting, auditing, fine-tuning, or a private deployment matters, check Moonshot’s current release status and license before committing. Hosted access and downloadable weights are separate decisions: a model can be usable in a web product or API while its weights are unavailable, incomplete, or subject to different terms.
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“Open-weight” also does not automatically mean inexpensive or simple to run. A deployment decision requires the actual weight files, license, hardware requirements, supported inference stack, quantization options, and operational performance. None of those details are established by the announcement’s headline description alone.
Context length and input types
Long context is valuable only when your workflow can supply useful material and the model can reliably retrieve the right details. A nominal limit is not the same as perfect attention throughout a document, nor does it remove the need for retrieval, chunking, or source validation.
| Model | Documented context or input capability | What the evidence supports |
|---|---|---|
| Kimi K3.1 Pro | 1-million-token context window | Moonshot states this limit in its announcement. It is relevant to large codebases, research collections, and long-running agent prompts. |
| Gemini 3.1 Pro | Up to 1 million tokens; text, images, audio, and video | Google DeepMind’s February 19, 2026 model card explicitly lists these modalities and the maximum context. |
| Claude | Not established in the evidence used here | Check the exact current model and plan documentation before comparing context limits. |
| GPT | Not established in the evidence used here | Check the exact current model, endpoint, and account tier before comparing context limits. |
When a million-token window helps
A large window can reduce the engineering needed to assemble a prompt containing multiple repositories, contracts, financial statements, meeting transcripts, or a long investigation. It can also preserve more conversation state in an agent loop. You still need a clear instruction hierarchy, references to the most important passages, and checks against omissions.
When it does not help
If your task is a short classification, a small code edit, or a simple answer, a huge context limit adds no quality by itself. Sending an entire archive can increase cost and make it harder to identify the authoritative source. For those jobs, a smaller prompt with carefully selected evidence may be faster, cheaper, and easier to audit.
What the available benchmark evidence actually shows
Kimi’s Plotly data-analytics result
Plotly’s October 23, 2026 report describes a specific data-analytics exam run on K3.1 Pro. It reports 42 correct answers out of 43, rounded by Plotly to 98%, a $13.47 cost for one complete run, and a 2.5-minute median time per question: Plotly’s benchmark report.
Rank #2
Plotly says its exam covers analytics, ambiguous questions, impossible requests, statistics traps, data-quality problems, and hidden patterns. That makes the result useful evidence for a reader whose work resembles that exam. It does not establish that Kimi is the best coder, writer, general reasoner, or agent on every task. The cost is for Plotly’s run, under Plotly’s prompt and execution setup; it is not a quote for your workload.
Gemini 3.1 Pro’s model-card results
Google DeepMind’s model card, published February 19, 2026, describes Gemini 3.1 Pro as a natively multimodal reasoning model and reports results under named test conditions: Gemini 3.1 Pro model card.
| Evaluation | Reported result | Setting stated by the card |
|---|---|---|
| Humanity’s Last Exam | 44.4% | Full set, text plus multimodal, no tools |
| ARC-AGI-2 | 77.1% | ARC Prize Verified |
| GPQA Diamond | 94.3% | No tools |
| SWE-Bench Verified | 80.6% | Single attempt |
These figures describe Gemini 3.1 Pro in the card’s dated configurations. They are not interchangeable with Plotly’s exam score: the tasks, prompts, scoring, tools, and attempt rules differ. A percentage from one benchmark cannot be subtracted from or ranked directly against a percentage from another.
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The available evidence does not identify current Claude or GPT versions with matched primary-source results, prices, context settings, tool harnesses, and attempt counts. Saying that one family “beats” another would imply a controlled comparison that has not been demonstrated. If a vendor publishes a result, record the model version, date, endpoint, tools, reasoning setting, number of attempts, and scoring rule before placing it beside another result.
API pricing and how to estimate your own cost
Moonshot’s current platform listing, accessed October 25, 2026, lists these Kimi API rates:
| Model listing | Input tokens | Output tokens | Speed statement |
|---|---|---|---|
| K3.1 Pro | $2.00 per million | $10.00 per million | Standard listing; no independent speed figure supplied here |
| K3.1 Pro Highspeed | $4.00 per million | $20.00 per million | Moonshot claims roughly three times the speed while retaining the same intelligence and 1-million-token context |
These are API token rates, not a cross-provider consumer-subscription comparison. They do not include your application’s storage, tool calls, retries, orchestration, logging, or other services. The Highspeed multiplier is a provider claim; treat it as something to validate under your traffic pattern rather than a guaranteed latency result.
Token-cost formula
Use (input_tokens ÷ 1,000,000 × input_rate) + (output_tokens ÷ 1,000,000 × output_rate). Count input and output separately, and measure real requests rather than relying on a single prompt estimate.
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| Illustrative monthly usage | K3.1 Pro calculation | K3.1 Pro cost | Highspeed cost |
|---|---|---|---|
| 1 million input + 200,000 output tokens | $2.00 + $2.00 | $4.00 | $8.00 |
| 10 million input + 1 million output tokens | $20.00 + $10.00 | $30.00 | $60.00 |
The examples are arithmetic illustrations using the listed rates, not a promise of a particular bill. Cache discounts, batching, minimums, regional terms, and future price changes must be checked on the live platform page. For Claude and GPT, obtain the exact current rate card for the endpoint you would use; no matched figures are established here.
Cost decisions that matter more than the headline rate
- Measure how many input tokens are repeated across turns and whether the provider offers a cache mechanism for your endpoint.
- Track output length. A verbose agent can cost more even when its input is unchanged.
- Include failed calls, retries, tool-result text, and parallel attempts.
- Separate development traffic from production traffic; exploratory prompts often have a very different token profile.
- Set a per-task budget and stop condition so an agent cannot silently consume an unbounded number of calls.
Latency, throughput, and agent behavior
Latency has at least three parts: time to first token, time to generate the answer, and time spent on tools or orchestration. A model that is fast for a short answer may be slower for a long analysis, while an apparently slower model can finish a multi-step workflow sooner if it needs fewer retries.
Moonshot’s “roughly three times faster” Highspeed description is a vendor statement. Plotly’s 2.5-minute median per question is an observed result from one exam. Neither number predicts your p95 latency for a streaming chat, repository edit, or tool-using agent.
How to run a useful latency test
- Build a fixed set of representative prompts, including short, long, easy, and failure-prone cases.
- Keep the model version, region, prompt, tools, reasoning settings, and output limit constant.
- Run enough repetitions to record median and tail latency, not just the fastest response.
- Record time to first token, total completion time, token counts, tool time, errors, and retries.
- Repeat at the traffic level you expect; queueing can change results dramatically.
Choosing by task
Coding and repository work
Kimi’s announcement specifically highlights coding and a million-token context, making it a candidate for large repositories, architecture questions, and agentic change requests. Gemini’s card reports an 80.6% SWE-Bench Verified result under a single-attempt setting, but that score belongs to the card’s exact harness and is not a direct Kimi result. For Claude or GPT, test the exact current coding model and tools you would deploy.
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Evaluate more than patch acceptance: compile or test success, security regressions, migration safety, explanation quality, and the number of human corrections required. A model that writes a plausible patch but misses repository conventions can cost more than one with a lower headline score.
Data analysis and statistics
Kimi has the strongest task-specific evidence in the available data because Plotly tested it on an analytics exam and reported 42/43 correct. If your work includes messy data, ambiguous requests, impossible analyses, or statistical traps, reproduce those conditions in a private evaluation. Check calculations with deterministic tools and inspect whether the model states assumptions instead of inventing certainty.
Do not treat the Plotly result as a substitute for testing your data sources, file formats, charting libraries, and privacy constraints.
Long-form research and document synthesis
Kimi and Gemini both document a 1-million-token maximum context. The deciding question is whether your application needs one large prompt or a retrieval pipeline with smaller, auditable passages. Test citation accuracy, contradiction handling, table extraction, and the ability to identify missing evidence. Long context can preserve more source material, but it does not guarantee faithful synthesis.
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Gemini 3.1 Pro’s model card explicitly lists text, image, audio, and video inputs. That makes it the clearest documented candidate here for a single multimodal request. The Kimi announcement’s capability examples do not establish the same input-modality list, so verify the specific Kimi endpoint before designing a multimodal workflow. Verify the exact Claude or GPT model and endpoint in the same way.
Writing and editing
Writing quality is highly sensitive to style instructions, source material, output length, and revision loops. None of the cited evidence supplies a matched writing evaluation across these four families. Create a blind test with your own examples: factual editing, tone conversion, structure, copy fitting, and refusal of unsupported claims. Score the outputs against a rubric rather than choosing from a reputation-based ranking.
Tool-using agents
For agents, model intelligence is only one component. The harness determines which tools are enabled, how tool errors are surfaced, how many attempts are allowed, whether the model can inspect intermediate state, and when the run stops. Record those variables in every comparison. A model card’s no-tool score cannot be used to predict a tool-rich agent’s result without testing.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A decision matrix you can apply before switching models
| Your requirement | Evidence-supported starting point | What to verify before production |
|---|---|---|
| Very large text or code input | Kimi K3.1 Pro or Gemini 3.1 Pro, both documented with a 1-million-token window | Actual usable limit, truncation behavior, retrieval accuracy, and cost at your token volume |
| Text plus image, audio, or video in one request | Gemini 3.1 Pro, whose card explicitly lists all four input types | Supported formats, media limits, latency, and output accuracy for your files |
| Data analytics with ambiguous or dirty inputs | Kimi K3.1 Pro is worth a direct pilot because of Plotly’s 42/43 exam result | Performance on your datasets, reproducibility, statistical validation, and total run cost |
| Lowest measured API spend | Calculate Kimi’s standard rate against your own input/output mix | Cache terms, retries, tool tokens, and the current rates of every finalist |
| Fast interactive responses | Test K3.1 Pro Highspeed alongside standard Kimi and your other finalists | Median and p95 latency under your concurrency; the three-times claim is not a guarantee |
| Self-hosting or fine-tuning | Investigate Kimi only after confirming the announced weights release and license | Weights, hardware, inference software, support, security, and update policy |
A repeatable evaluation procedure
- Define success. Write measurable acceptance tests: factual accuracy, test pass rate, extraction fields, latency, cost, or human-edit time.
- Freeze the task set. Use real but privacy-safe examples, including edge cases and known failures.
- Normalize the harness. Keep system instructions, tools, temperature or equivalent controls, context, output limits, and attempt counts consistent.
- Log everything. Save model identifier, date, prompt version, token counts, tool calls, errors, latency, and evaluator notes.
- Score blind where possible. Hide the model name from reviewers to reduce brand bias.
- Calculate total cost. Include retries, long outputs, tool results, and human review, not only the provider’s token invoice.
- Retest before launch. Model versions, prices, access routes, and benchmark conditions change; a dated result is a snapshot.
Safety and data handling checks
- Confirm whether prompts and outputs may be retained or used for service improvement under the account and endpoint you select.
- Remove secrets, personal data, and regulated information unless your contract and configuration permit the use.
- Test prompt-injection resistance when documents or web pages are untrusted.
- Require human approval for destructive code changes, financial decisions, and external actions.
Common comparison mistakes
Turning unlike percentages into a leaderboard
A 98% result on Plotly’s exam and a 94.3% GPQA Diamond result measure different abilities under different conditions. Present each with its evaluator, date, task, and setting.
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Web access, an API, and downloadable weights have different operational, legal, and security implications. Confirm what is actually available rather than inferring it from “open-weight” wording or an announced date.
Using subscription prices to estimate API cost
A consumer plan may have usage limits and features unrelated to token billing. Use the endpoint-specific rate card and your measured token profile.
Best Value
Assuming context capacity equals reliability
Long prompts still need source ordering, retrieval checks, and contradiction tests. Test the model’s ability to find and quote the right passage at the position where your data puts it.
Ignoring version dates
Model names, endpoints, prices, and benchmark implementations can change quickly. Put the access date and exact identifier in your evaluation records.
Bottom line for each type of buyer
If you are an analyst or data team
Pilot Kimi K3.1 Pro against your real analytics cases, because the Plotly result is directly relevant but narrowly scoped. Keep deterministic validation and human review around every consequential calculation.
If you handle mixed media
Start with Gemini 3.1 Pro because its model card explicitly documents text, image, audio, and video input. Confirm file limits and your required accuracy before scaling.
If you build a coding or agent platform
Shortlist Kimi and Gemini, then compare the exact Claude and GPT endpoints you can access using one fixed harness. Measure completed workflows, not isolated answers.
If you need a private deployment
Do not commit on the basis of an announcement. Verify Kimi’s weights-release status after the stated November 4 date, the license, hardware needs, and production tooling, then compare those requirements with the alternatives available to you.
If you simply want the best general model
The available evidence cannot support that label. Run a small, representative bake-off and choose the model that meets your quality, cost, latency, data-handling, and availability requirements at the same time.
Quick Recap
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