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Short answer: JetBrains’ published comparison does not establish one universal winner. Against the specific Qwen3.5 (9B) checkpoint, Mellum2.1 Thinking leads on LiveCodeBench v6, HumanEval+, MBPP+, BFCL v4 and WorkBench. Qwen3.5 (9B) leads on SWE-bench Verified, Terminal-Bench 2.1, SWE-bench Pro and ToolHop. That split matters: coding-agent performance depends heavily on whether you need code generation, repository repair, terminal work or tool calling.
The phrase “Qwen Coder” is also ambiguous. The official comparison names Qwen3.5 (9B), not an unspecified model family or every Qwen checkpoint. The conclusions below therefore apply to Mellum2.1 Thinking versus Qwen3.5 (9B), using JetBrains’ reported test setup.
What is being compared?
Mellum2.1 is JetBrains’ open-weight coding model for coding agents, sub-agents and self-hosted workflows. JetBrains describes it as a mixture-of-experts model with 12 billion total parameters and 2.5 billion active parameters, released under the Apache 2.0 license. Its model card lists a 131,072-token context length and documents serving through vLLM, including a tool-calling configuration. See the Mellum2.1 Thinking model card for the published specification and serving details.
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Why the Qwen name needs qualification
“Qwen Coder” is not a precise checkpoint name in JetBrains’ comparison. JetBrains’ table uses Qwen3.5 (9B). Results for another Qwen coding model, a different size, a quantized build or a different inference prompt should not be silently substituted for these figures. If you are choosing a model for production, record the exact repository, revision, prompting format and runtime you test.
At-a-glance differences
| Characteristic | Mellum2.1 Thinking | Qwen comparator in JetBrains’ table |
|---|---|---|
| Model identified by the comparison | Mellum2.1 Thinking, the thinking variant of Mellum2.1 | Qwen3.5 (9B) |
| Architecture and size | 12B total parameters; 2.5B active parameters; mixture of experts | Not stated in the cited comparison |
| Listed context length | 131,072 tokens | Not stated in the cited comparison |
| License | Apache 2.0 | Not stated in the cited comparison |
| Self-hosting documentation | vLLM serving path and tool-calling configuration are shown in the model card | Not stated in the cited comparison |
| Primary positioning | Coding agents, fast sub-agents and local deployment | Compared as a coding model in the same evaluations |
The active-parameter number is not a complete memory requirement. Mixture-of-experts serving still involves model weights, runtime state, context, batching and implementation overhead. JetBrains does not specify a hardware configuration that guarantees full-context serving, so do not treat 2.5B active parameters as a promise that the model will fit in a particular graphics card or system.
Published benchmark results
The following numbers come from JetBrains’ official model card. They are publisher-reported results, not an independent replication.
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| Evaluation | Mellum2.1 Thinking | Qwen3.5 (9B) | Higher reported score |
|---|---|---|---|
| LiveCodeBench v6 | 82.0 | 75.4 | Mellum2.1 Thinking |
| HumanEval+ | 91.5 | 89.6 | Mellum2.1 Thinking |
| MBPP+ | 79.4 | 69.8 | Mellum2.1 Thinking |
| SWE-bench Verified | 47.0 | 50.0 | Qwen3.5 (9B) |
| Terminal-Bench 2.1 | 17.4 | 21.7 | Qwen3.5 (9B) |
| SWE-bench Pro | 28.0 | 38.0 | Qwen3.5 (9B) |
| BFCL v4 | 62.3 | 58.5 | Mellum2.1 Thinking |
| WorkBench | 44.6 | 39.7 | Mellum2.1 Thinking |
| ToolHop | 49.1 | 52.0 | Qwen3.5 (9B) |
These tests measure different behaviors, so adding the scores together would produce a misleading ranking. HumanEval+ and MBPP+ are closer to standalone programming exercises. LiveCodeBench v6 samples competitive-programming-style tasks. SWE-bench evaluations test repository-level issue resolution. Terminal-Bench emphasizes work performed through a terminal, while BFCL and ToolHop probe tool-use behavior. WorkBench is another agent-oriented evaluation in JetBrains’ table.
How large are the reported gaps?
The differences are meaningful but uneven. Mellum2.1 is 6.6 points ahead on LiveCodeBench v6, 1.9 points on HumanEval+ and 9.6 points on MBPP+. Qwen3.5 is 3 points ahead on SWE-bench Verified, 4.3 points on Terminal-Bench 2.1 and 10 points on SWE-bench Pro. The largest Mellum2.1 advantage in the table is MBPP+; the largest Qwen advantage is SWE-bench Pro. Neither pattern proves that the same margin will appear on your private repositories. Dataset composition, prompt format, test coverage and retry policy can all change practical ranking.
How JetBrains ran the tests
The evaluation setup explains why the numbers should be read as a controlled comparison rather than a universal leaderboard.
Non-agentic evaluations
JetBrains’ model card says non-agentic benchmarks used greedy decoding. Greedy decoding selects the highest-probability next token at each step and removes sampling variability, but it does not represent every application’s production settings. A deployment using temperature, multiple candidates, retries or a different system prompt can produce different results.
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Agentic evaluations
For agentic tests, the models used the same open-source Pi v0.73.1 harness, shell and file tools, a 114K-token context and a maximum of 16K tokens per turn. Mellum2.1 used its default temperature of 1.0. JetBrains also says Mellum2 Thinking was re-evaluated with this pipeline, which is why its figures differ slightly from the technical report. The harness, context budget and turn limit are part of the result; changing any of them can change success rates.
When reproducing the comparison, keep the harness version, tool schemas, context limit, turn budget, model revision and decoding settings fixed. Otherwise, a difference attributed to the model may actually come from orchestration or inference configuration.
What the split results mean for coding work
Choose Mellum2.1 when code generation and tool calling dominate
Mellum2.1 Thinking has the higher reported scores on LiveCodeBench v6, HumanEval+ and MBPP+, which favors workloads resembling algorithmic problem solving and self-contained code generation in this table. Its higher BFCL v4 and WorkBench scores also make it the more attractive candidate when structured tool invocation or the particular WorkBench-style tasks resemble your agent’s job.
This does not prove that it will be better at every new function, language or repository. Benchmark distributions, prompts and stopping rules matter. Treat the scores as evidence for a starting hypothesis, then test representative tasks from your own codebase.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesChoose Qwen3.5 (9B) when repository repair and terminal work dominate
Qwen3.5 (9B) has the higher reported scores on SWE-bench Verified, Terminal-Bench 2.1 and SWE-bench Pro. Those results make it the stronger first candidate for agents that must diagnose an existing repository, modify multiple files, run commands and resolve issue-style tasks. It also leads ToolHop in the published table, so it deserves testing for workflows that require a sequence of tool interactions.
The margin on a benchmark is not a guarantee of faster or safer work in your environment. Repository size, language, test quality, shell permissions and the agent harness can outweigh a leaderboard difference.
Do not select by a single headline score
A coding assistant that writes short functions may need a different model from an autonomous maintenance agent. A model that scores well on a terminal benchmark may still need guardrails around destructive commands. Conversely, a model with strong code-generation scores may require more orchestration to navigate a large monorepo. Map each benchmark category to an actual task before deciding.
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Deployment and model architecture
Mixture-of-experts implications
Mellum2.1’s 12B total and 2.5B active parameter figures describe different aspects of computation. The active count indicates how many parameters are used for a token’s selected experts, while the total count describes the complete expert pool. Memory use, loading time and throughput depend on the stored weights, precision, cache size, batching and serving engine, not only on active parameters.
Context length versus usable context
The model card lists 131,072 tokens, while JetBrains’ agent evaluation used a 114K-token context. Those are not contradictory: the first is the listed model limit, and the second is the context budget chosen for the test harness. A long advertised context also does not guarantee equal quality at every position. Measure retrieval, editing and instruction-following on the codebase sizes you actually use.
Serving with vLLM
The Mellum2.1 model card provides a vLLM serving path and a tool-calling configuration. Follow that card for the current model identifier, template and runtime arguments rather than copying a command from an older post. Validate that your chosen vLLM release supports the model’s architecture, chat template and tool-call format before connecting it to an agent.
No hardware requirement is specified in the cited JetBrains material. Plan a small compatibility test that measures model-load success, peak memory, prompt processing, generation speed, tool-call correctness and stability at your intended context length. Do not promise full-context operation until that test succeeds on the exact hardware and precision you plan to deploy.
License and operational responsibility
Mellum2.1 is released under Apache 2.0 according to JetBrains’ model card. That is permissive, but it does not remove your obligations around notices, internal security controls, data handling, model access and the licenses of code or datasets used by your application. Check the model card and your organization’s legal policy before distributing a service or modified weights.
Cost, capacity and latency planning
Neither cited JetBrains source supplies a purchase price, hosted API rate or validated hardware bill for either model. For a self-hosted comparison, “cost” is therefore an operational measurement rather than a number that can be copied from the benchmark table. Include accelerator or server time, storage for model weights, electricity, engineering time, monitoring and the cost of failed or retried agent runs.
Measure capacity at the concurrency you actually expect. A model that is economical for one developer may require more replicas when several agents send long 114K-token prompts simultaneously. Conversely, a smaller active-parameter path may improve compute efficiency without reducing the memory needed to store all experts. Record prompt-token processing separately from generated-token throughput, and report time to first token as well as total completion time.
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Retries also affect effective cost. If one model completes 70 percent of repository tasks on the first attempt and another completes 60 percent but needs fewer expensive long-context retries, the cheaper-looking per-token result may not be the cheaper workflow. Track successful patches per hour, successful patches per dollar or server-hour, and the human review time each model creates.
Tool calling and safety requirements
The BFCL and ToolHop results are useful signals, but a passing tool-call benchmark does not authorize unrestricted execution. In a real agent, validate tool names and argument schemas, reject unknown paths, limit shell commands, isolate secrets and require approval for destructive operations. Run tests in a sandbox with network and filesystem boundaries appropriate to the repository.
Inspect the complete action trace, not only the final answer. A model can produce a correct patch after unnecessary file edits, leak sensitive data into logs or invoke a command that happens to succeed in a benchmark container. Store audit logs, cap turn counts and token budgets, and make rollback straightforward. These controls apply equally whether Mellum2.1 or Qwen3.5 is selected.
JetBrains’ reported speed claims
JetBrains’ announcement says that, under heavy load, Mellum2.1 served almost twice as many tokens as Qwen3.5-9B, and that multi-token prediction made a single request about 1.6 times faster. These are claims made by JetBrains in its Mellum2.1 announcement. The accessible announcement text does not provide all plotted measurement details, such as the complete hardware, batch, sequence-length and concurrency conditions, so the figures should not be treated as independently established benchmarks.
Throughput and single-request latency answer different questions. A server handling many simultaneous users can favor tokens-per-second under load, while an interactive developer cares about time to first token, time to a complete patch and the delay between tool calls. Measure both, along with error rate and output quality, on your serving stack.
A practical evaluation plan for your own agent
Use the published scores to choose candidates, then run a controlled bake-off. The following process avoids confusing model quality with harness or infrastructure differences.
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- Pin the exact checkpoints. Record Mellum2.1 Thinking and Qwen3.5 (9B) repository identifiers, revisions, tokenizer files and license text. If “Qwen Coder” in your environment means a different checkpoint, label that comparison separately.
- Create a representative task set. Include standalone functions, bug fixes, multi-file features, dependency upgrades, test repair, shell commands and tool calls. Keep private code and credentials out of prompts unless your security policy explicitly permits them.
- Use the same agent harness. Keep system instructions, tool schemas, context budget, maximum turns, retry policy and file-access permissions identical. The Pi v0.73.1 setup in JetBrains’ evaluation is a useful reference point, but your own harness may behave differently.
- Define success before running. For repository tasks, require tests to pass and inspect the diff for scope, security and maintainability. For tool use, verify that the correct tool, arguments and order were selected. For code generation, run language-specific tests and static checks.
- Run repeated trials where sampling is enabled. Record temperature, seed policy and number of attempts. A single successful run can hide reliability problems; report pass rate and the number of retries needed.
- Measure operational metrics. Capture time to first token, completion latency, prompt and generation throughput, peak memory, context length, tool-call failures, abandoned runs and cost of retries. Keep heavy-load and single-user tests separate.
- Review the patches manually. Check for unrequested edits, brittle tests, insecure shell commands, hallucinated APIs, license problems and changes that merely mask a failing test. A benchmark-style pass is not a production approval.
- Make a task-specific decision. Select the model with the best combined quality and operational result for your workload. If the workloads differ, route them separately instead of forcing one model to handle every task.
Common mistakes when interpreting this comparison
- Turning “Qwen Coder” into a whole-family verdict. The cited table compares Qwen3.5 (9B), not every Qwen coding checkpoint.
- Declaring a universal winner. The lead changes across code generation, repository repair, terminal work and tool-use tests.
- Mixing evaluation protocols. Agentic results used Pi v0.73.1, shell and file tools, 114K context and up to 16K tokens per turn. Non-agentic tests used greedy decoding.
- Reading active parameters as a hardware specification. The 2.5B active figure does not establish a complete memory requirement or a guaranteed full-context deployment.
- Repeating speed claims without conditions. “Almost twice as many tokens” and “about 1.6 times faster” are JetBrains’ reported results whose full plotted test conditions are not exposed in the accessible announcement text.
- Assuming benchmark scores predict safe autonomy. Add command allowlists, filesystem boundaries, approval gates, secrets isolation, test execution limits and audit logs before granting an agent write access to valuable repositories.
Which model should you start with?
Start with Mellum2.1 Thinking if your priority is an Apache-licensed, self-hosted model with strong publisher-reported results on coding benchmarks, tool calling and the WorkBench evaluation, or if its documented vLLM path fits your stack. Start with Qwen3.5 (9B) if your primary workload resembles SWE-bench issue resolution, terminal-driven debugging or ToolHop-style multi-step tool use.
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For a serious deployment, run both on a fixed slice of your repositories. Keep the checkpoint identity and agent configuration explicit, separate quality from latency measurements and treat safety review as a release requirement. The published evidence supports a task-based choice, not a blanket claim that one model replaces the other.
Sources
- JetBrains: Mellum2.1 Gets to Work for Coding Agents
- JetBrains Mellum2.1 Thinking model card
- JetBrains: Mellum2 Goes Open Source
Frequently Asked Questions
Is Mellum2.1 better than Qwen Coder?
There is no universal winner in JetBrains’ published table. Mellum2.1 Thinking scores higher on LiveCodeBench v6, HumanEval+, MBPP+, BFCL v4 and WorkBench, while Qwen3.5 (9B) scores higher on SWE-bench Verified, Terminal-Bench 2.1, SWE-bench Pro and ToolHop.
Which Qwen model is actually compared?
The named comparator is Qwen3.5 (9B). “Qwen Coder” could refer to another checkpoint, size or quantization, so verify the exact repository and revision before applying these results.
Can 2.5B active parameters run on a small GPU?
The active-parameter figure alone cannot answer that. Total stored weights, precision, context cache, batching and serving overhead matter, and JetBrains does not publish a hardware configuration that guarantees full-context serving.
What context length should an agent use?
Mellum2.1’s model card lists 131,072 tokens, but JetBrains used a 114K-token budget for agentic tests. Treat those as different limits and measure quality, memory and latency at the context size your workload requires.
Are JetBrains’ speed claims independently verified?
No independent replication is established here. JetBrains reports almost twice the token throughput under heavy load and about 1.6-times faster single-request performance with multi-token prediction, but the accessible announcement does not expose every test condition.
How should I evaluate the models before deployment?
Pin exact checkpoints, use the same harness and tools, test representative repository and coding tasks, repeat sampled trials, measure quality and operational metrics, inspect diffs manually and enforce sandboxing, approval gates and audit logs.
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Bottom line: Compare Mellum2.1 Thinking with the specifically named Qwen3.5 (9B), not with an undefined “Qwen Coder” category. JetBrains’ results favor Mellum2.1 for several code-generation and tool-calling tests, and Qwen3.5 for repository, terminal and SWE-bench work. The practical winner is the model that delivers the best quality, latency, reliability and safety on your own tasks.
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