There is no universal lowest-token winner. A five-task comparison published by Dataku in 2025 measured AutoGen at the lowest average (34,280 tokens), while an AIMultiple benchmark summary published in 2026 placed AutoGen slightly above LangGraph and LangChain and found CrewAI used nearly twice as many tokens as the other frameworks in that test setup. The results conflict because token use is driven by the model, prompts, tools, memory, retries, and termination rules as much as by the framework.
If your workload resembles Dataku’s tasks, AutoGen is a sensible candidate to test first. If it resembles AIMultiple’s setup, LangGraph or LangChain may be cheaper, while CrewAI may incur more coordination overhead. The only dependable way to choose is to run representative tasks under identical conditions and measure both tokens and successful outcomes.
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What “fewest tokens” actually measures
A framework does not have a fixed token price. Your billable usage is the sum of tokens sent to and generated by the model during a run. Depending on the provider, input and output tokens can have different prices, and cached or batch tokens may be priced differently again. A useful accounting model is:
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Some of those tokens are obvious in a model API response. Others are hidden in orchestration: a supervisor may restate a task for each worker, a tool result may be inserted into the next prompt, or a retry may resend the entire conversation. A framework that looks lightweight in source code can therefore consume more tokens if it carries long histories, while a more feature-rich framework can be efficient when it passes only concise state updates.
Token count is also not the same as cost or quality. A run that uses 20 percent fewer tokens but fails twice as often can cost more per successful task. Compare token totals with success rate, latency, retry frequency, and the value of the completed work.
What the published comparisons found
The available published figures are useful signals, not a permanent ranking. Dataku and AIMultiple used different models, prompts, tools, framework versions, and task definitions, so their numbers cannot be merged into one leaderboard.
| Source and date | Framework or result | Reported usage | How to interpret it |
|---|---|---|---|
| Dataku, 2025 | AutoGen | 34,280 average tokens across five tasks | Lowest average in that five-task comparison. |
| Dataku, 2025 | CrewAI | 37,720 average tokens across the same five tasks | Higher than AutoGen but lower than LangChain in that test. |
| Dataku, 2025 | LangChain | 42,880 average tokens across the same five tasks | Highest of the three values reported by Dataku; this is LangChain, not LangGraph. |
| AIMultiple, 2026 | Four-framework benchmark program | 2,000 benchmark runs across five tasks | A larger summarized program, but its exact model, prompts, tool setup, and versions differ from Dataku’s. |
| AIMultiple, 2026 | CrewAI | Nearly twice the tokens of the other frameworks in the summarized setup | Indicates substantial coordination overhead in that design; it does not prove CrewAI is always last. |
Dataku’s comparison did not report LangGraph in the three averages above, so its LangChain result should not be presented as a LangGraph result. AIMultiple’s summary did include LangGraph, but it ranked frameworks under a different experimental design. No independently reproducible, current apples-to-apples test covering exactly LangGraph, AutoGen, and CrewAI with identical models, prompts, tools, and versions establishes a permanent winner.
Why token rankings change from one test to another
Prompt history and message topology
Every model call may include system instructions, the user request, prior assistant messages, tool results, and messages from other agents. A conversational design often forwards the full transcript to several participants. A graph can instead pass a compact state object or a delta containing only the fields a node needs. Either approach can be efficient or wasteful depending on implementation.
Generated completions
Long plans, verbose explanations, repeated JSON fields, and chain-of-thought-like internal output all increase completion tokens. A framework’s defaults may encourage one agent to explain work to another rather than return a short structured result. Output schemas, concise prompts, and explicit length limits can reduce this component without changing the orchestration library.
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Tool calls and payloads
Tool arguments and results are normally serialized into the next model prompt. Returning an entire database row set, web page, or log file can dominate usage even when the agent’s answer is short. Pagination, field selection, server-side filtering, and summaries reduce tokens in every framework.
Retries and error recovery
Validation failures, rate-limit retries, timeouts, and tool errors can resend a large prompt. A framework with aggressive automatic retries may show high token use while achieving a good success rate. Report both raw usage and usage per successful task, along with the number and cause of retries.
Memory, checkpoints, and context limits
Persistent memory and graph checkpoints improve recoverability, but replaying them into later calls adds input tokens. Summarizing old turns, storing embeddings or structured facts instead of prose, and loading only relevant records keeps context bounded. Context-window pressure can also trigger truncation or extra summarization calls, which changes both usage and behavior.
Termination behavior
An agent that continues until a conversational consensus is reached may make more calls than a workflow that stops after a validated result. Maximum turns, explicit stop conditions, and a final output contract are therefore major cost controls.
How each framework can create or avoid overhead
LangGraph
LangGraph, from LangChain, models a workflow as a directed state graph with explicit nodes and transitions. You decide which node runs, what state it reads, what it writes, and which branch follows a condition. This makes it possible to pass narrow state deltas, place validation before an expensive model call, and terminate deterministically after a known sequence.
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The same control can be used inefficiently. If each node receives the complete message history, or if a loop repeatedly appends verbose tool output to shared state, token usage grows with every iteration. Checkpoint persistence can add replayed context when recovering a run. LangGraph is therefore a strong choice when you can design a bounded state schema and explicit branching, not an automatic guarantee of low usage.
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AutoGen
AutoGen exposes conversational multi-agent patterns and model-client usage accounting. Microsoft’s documentation states: The
That observability helps you identify which agent or call is consuming the budget.OpenAIWrapper from autogen tracks token counts and costs of your API calls.
AutoGen can be economical when agents exchange concise messages and a controller stops the conversation quickly. It can be expensive when several agents repeat the same task description, quote the full transcript, or debate without a strict termination condition. Configure turn limits, concise role instructions, structured handoffs, and per-agent budgets. Treat the built-in usage summary as an accounting aid and verify that tool payloads and retries are included in your own telemetry.
CrewAI
CrewAI uses role-based multi-agent orchestration: agents have roles, goals, and tasks, and a crew coordinates their work. This is convenient for workflows that map naturally to specialist roles, such as researcher, analyst, and editor.
Role descriptions, task context, delegation messages, and result handoffs can create a large prompt footprint. The nearly twofold usage reported by AIMultiple shows how costly that pattern can become in one benchmark design. It does not mean every CrewAI crew has that overhead. Use sequential tasks when parallel debate is unnecessary, keep role and task text short, pass structured summaries rather than transcripts, and disable delegation that adds no decision value.
How to run a fair token benchmark
Benchmarking your own workload is more informative than choosing from a published ranking. Keep every variable that can affect usage constant except the framework.
- Define a representative task set. Include the normal mix of easy, difficult, tool-heavy, branching, and failure-prone cases. Five toy prompts can hide behavior that appears in production.
- Freeze the model configuration. Use the same model, provider, version, temperature or equivalent sampling settings, context limit, and structured-output mode for every framework.
- Use identical instructions and tools. Port the same system goals, user prompts, tool schemas, tool data, permissions, and result formats. Do not give one framework a shorter prompt or a more capable helper function.
- Set equivalent control policies. Match maximum turns, timeout values, retry counts, parallelism, memory retention, and termination criteria. If a framework cannot implement an equivalent policy, document the difference instead of hiding it.
- Warm up and randomize. Run enough warm-up calls to remove initialization effects, then randomize framework order so temporary provider conditions do not favor one implementation.
- Repeat each task. Report the number of runs and use medians, percentiles, and spread in addition to the mean. A single average can conceal occasional runaway loops.
- Capture a complete ledger. Record input tokens, output tokens, tool-call arguments and results, agent-to-agent messages, retries, model-call count, latency, and termination reason for every run.
- Score the result. Mark task success with a predefined rubric. Report tokens per attempted task and tokens per successful task; the latter exposes the cost of failures and retries.
- Repeat after optimization. First compare default implementations, then apply the same class of optimization—such as a shorter prompt or a stricter turn limit—to each framework and rerun.
A useful report includes the exact framework versions and date, because orchestration behavior changes as libraries evolve. It should also distinguish one-time setup calls from recurring production calls.
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How token use translates into cost
To estimate spend, separate input and output usage:
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Run cost = (input tokens ÷ 1,000,000 × input price) + (output tokens ÷ 1,000,000 × output price) + provider charges for tools or other services.
Insert the prices for your chosen model and region; they are not framework properties. Then multiply by the expected number of runs and add the cost of failed attempts, retries, and scheduled background jobs. A framework with a higher token count can still be cheaper if it uses a lower-priced model, succeeds more often, or avoids expensive external tools. Conversely, a small token difference matters little when a single failed tool call triggers a costly downstream process.
Ways to reduce usage in LangGraph
- Design a narrow state schema. Store IDs, decisions, extracted fields, and short summaries instead of full transcripts or raw documents.
- Trim at node boundaries. Before passing state to a model, select only the fields that node needs. Keep raw tool output outside the prompt when possible.
- Put cheap checks first. Use deterministic validation, routing, and duplicate detection before invoking another model.
- Bound loops. Set a maximum iteration count and a clear success condition; route irrecoverable errors to a final failure state.
- Control checkpoint replay. Persist enough state to resume safely, but summarize or compress old conversational content before it is reloaded.
Ways to reduce usage in AutoGen
- Limit conversation turns. Set a hard maximum and stop immediately when a validated answer or tool result is available.
- Use concise handoffs. Require agents to return a structured decision, evidence IDs, and next action rather than a narrative recap.
- Separate planning from execution. Let one planner produce a short plan, then have workers execute only their assigned step instead of repeating the entire objective.
- Inspect usage summaries. Attribute input and output tokens to each model client or agent so the largest contributor is visible.
- Make retries selective. Retry transient transport failures, not deterministic validation errors; shorten the retry context when a retry is necessary.
Ways to reduce usage in CrewAI
- Keep role and goal text specific. Long persona descriptions are repeated context on every task.
- Pass summaries between tasks. Give the next agent a schema with the findings it needs, not the previous agent’s full transcript.
- Choose the simplest process. Use sequential execution when parallel collaboration does not improve the result, and avoid delegation for a task that has one clear owner.
- Constrain outputs. Require bounded lists, fixed fields, or a short recommendation with evidence references.
- Measure coordination separately. Count tokens used for delegation and handoff messages so you can see whether role structure is buying enough quality to justify its cost.
Decision matrix: which framework fits which workload?
| Priority | Most relevant capability | What to examine | Token risk to watch |
|---|---|---|---|
| Deterministic branching and bounded workflows | LangGraph’s explicit nodes, transitions, and state | State size, loop limits, checkpoint replay | Full-history propagation between nodes |
| Conversational specialist collaboration | AutoGen’s agent conversations and usage summaries | Turn caps, handoff format, per-agent accounting | Repeated transcripts and open-ended debate |
| Role-based task delegation | CrewAI’s roles, goals, and crew process | Delegation policy, task context, summary handoffs | Persona, delegation, and coordination messages |
| Lowest cost per successful task | Measured outcome, not a framework label | Tokens, success rate, retries, latency, tool cost | Optimizing raw tokens while failures rise |
| Operational observability | Per-call token and cost telemetry | Input/output split, model-call IDs, termination reason | Missing tool or retry usage in totals |
How to interpret the conflicting published results
Use Dataku’s 34,280-token AutoGen average as evidence that AutoGen can be efficient for a five-task workload, not as a promise for your application. The same comparison measured CrewAI at 37,720 and LangChain at 42,880, but it did not establish a LangGraph figure in those averages.
Use AIMultiple’s 2,000-run program as evidence that architecture and implementation can reverse the ranking. Its summarized setup placed AutoGen above LangGraph and LangChain and reported CrewAI at nearly twice the usage of the other frameworks. The difference may reflect prompts, model choice, tools, version differences, task composition, or coordination and retry policies; the summary does not justify attributing the entire gap to the framework alone.
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Practical recommendations by workload
Fixed business workflows
For a process such as classify an application, retrieve records, validate fields, and produce a decision, start with a graph-style design. Explicit transitions make it easier to cap calls and pass only the fields each step needs. AutoGen or CrewAI can still work, but require equivalent controls and a measured reason to add conversational coordination.
Research requiring several specialists
AutoGen or CrewAI may reduce implementation effort when independent specialists genuinely need to exchange findings. Keep exchanges structured, limit the number of rounds, and compare the quality gain with the additional coordination tokens. If the collaboration can be expressed as a known sequence, a LangGraph implementation may achieve the same result with smaller state deltas.
Long-running, recoverable operations
Favor the framework whose persistence and observability meet your recovery requirements, then control replay size. A small token saving is not worth losing the ability to resume safely after a tool or infrastructure failure. Measure checkpoint and recovery runs separately from clean runs.
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Run a mixture of short and difficult cases and examine the distribution. A framework that wins the average may lose the tail because of runaway conversations or repeated retries. Set per-run token budgets and a safe failure path before exposing the system to unbounded input.
Common benchmarking mistakes and fixes
- Comparing different models. Fix: pin the same model and provider configuration.
- Counting only completion tokens. Fix: include prompt history, tool payloads, retries, and agent messages.
- Using different task prompts. Fix: preserve semantic and textual equivalence as closely as the framework allows.
- Ignoring failures. Fix: report success rate and tokens per successful task.
- Reporting one run. Fix: repeat tasks and show median and spread.
- Mixing setup with production traffic. Fix: label one-time initialization separately from recurring calls.
- Assuming defaults are equivalent. Fix: match turn limits, memory, termination, and retry policies.
- Calling LangChain results LangGraph results. Fix: name the exact library tested; the published 42,880-token figure is for LangChain.
Bottom line for choosing a framework
Choose AutoGen first when your measured workload resembles the five tasks in Dataku’s 2025 comparison and its conversational controls fit your design. Choose LangGraph when explicit state, branching, persistence, and bounded execution let you keep prompts small. Choose CrewAI when role-based delegation materially improves delivery and your measurements show that its coordination overhead is justified.
Before committing, build the smallest representative benchmark you can: identical model and prompts, identical tools and limits, repeated runs, full token telemetry, and a success rubric. The framework with the fewest raw tokens is not necessarily the framework with the lowest cost per successful result.
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