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Spark-X2.6-4B by SparkLLM scores 39.82 overall and carries a context window of 1M tokens — observed 2026-10-23.
SparkLLM · Open Weight · rank 144 · bench-align-v5
Self-hostedSeven axes from the ranking source. A missing axis is drawn as a gap.
Measured values with the date they were observed. Nothing is inferred from a sibling model or a provider claim.
Time to first token: latency to the first answer chunk (Artificial Analysis, via BenchLM). Reasoning models include thinking time, so values can run to tens or hundreds of seconds.
Evidence key: ObservedUnavailable
| Measurement | Value | Observed | Last good | Evidence |
|---|---|---|---|---|
| Time to first token | Unavailable | Unavailable | Unavailable | |
| Throughput | Unavailable | Unavailable | Unavailable |
Regional or per-endpoint measurements appear only when the API supplies them; none are modelled here.
Every published price component, including cache reads and writes.
10 conversations per day × 8 messages × 22 active days, 1200 input and 400 output tokens per message, no cache. This uses the same calculator as the cost simulator, so an unavailable applicable rate makes the example unavailable too.
21 matched benchmark rows with their published value, unit, and provenance.
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| GPQA Graduate-Level Google-Proof Q&A | 69.9 | 448 questions | Graduate level | GPQA: A Graduate-Level Google-Proof Q&A Benchmark |
| GPQA-D GPQA Diamond | 77.1 | Graduate-level science questions | Graduate level | GPQA: A Graduate-Level Google-Proof Q&A Benchmark |
| HLE w/o tools Humanity's Last Exam without tools | 17.9 | Expert-level questions | Frontier expert level | Introducing GPT-5.4 mini and nano |
| Benchmark |
|---|
Lifecycle events the source associates with this model, plus what this profile does not claim.
That is not evidence the model has no lifecycle plan — only that this source published none.
| Value |
|---|
| Tasks |
|---|
| Difficulty |
|---|
| Provenance |
|---|
| SWE-bench Verified Software Engineering Benchmark Verified | 62.8 | 500 verified issues | Professional software engineering | SWE-bench: Can Language Models Resolve Real-World GitHub Issues? |
| SWE-bench Pro SWE-bench Pro | 54.3 | 1,865 repository problems | Long-horizon professional engineering | SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks? |
| SWE Multilingual SWE Multilingual | 64.7 | Multilingual software-engineering tasks | Professional software engineering | MiniMax M2.7: Early Echoes of Self-Evolution |
| SciCode Scientific Code Benchmark | 36.8 | — | — | BenchLM |
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| AIME26 AIME 2026 | 91.2 | Competition math problems | Olympiad-style mathematics | Qwen3.6 launch benchmarks |
| HMMT Feb 2026 Harvard-MIT Mathematics Tournament February 2026 | 81.3 | Competition math problems | Olympiad-style mathematics | Qwen3.6 launch benchmarks |
| IMOAnswerBench IMOAnswerBench | 75.2 | Advanced mathematical answer generation | Olympiad-level mathematics | DeepSeek-V4 Technical Report |
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| LongBench v2 LongBench v2 | 53.3 | Long-context tasks | Hard long-context | LongBench v2 |
| AA-LCR Artificial Analysis Long Context Reasoning | 67.3 | Long-context reasoning tasks | Long-context reasoning | Artificial Analysis model benchmarks |
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| IFEval Instruction-Following Eval | 93.6 | 541 prompts across 25 instruction types | Instruction precision | Instruction-Following Evaluation for Large Language Models |
| IFBench Instruction Following Benchmark | 75.8 | — | — | BenchLM |
| Benchmark | Value | Tasks | Difficulty | Provenance |
|---|---|---|---|---|
| BrowseComp BrowseComp | 53.1 | Research questions requiring browsing | Hard web research | BrowseComp |
| HLE w/ tools Humanity's Last Exam with tools | 36.9 | Expert questions with tool use | Frontier tool-augmented reasoning | DeepSeek-V4 Technical Report |
| MCP Atlas MCP Atlas | 62.4 | Tool-integrated agent tasks | Advanced tool use | Introducing GPT-5.4 mini and nano |
| τ²-bench results τ²-Bench Tool-Agent-User Evaluation | 86.9 | Airline, retail, and telecom customer-service task sets | Dual-control customer-service workflows | τ²-Bench: Evaluating Conversational Agents in a Dual-Control Environment |
| BFCL v4 Berkeley Function Calling Leaderboard v4 | 71.8 | Function-calling tasks | Advanced tool use | The Berkeley Function Calling Leaderboard (BFCL): From Tool Use to Agentic Evaluation of Large Language Models |
| Claw-Eval Claw-Eval | 69.9 | 300 tasks, 2,159 rubrics | Real-world general, multi-turn, and native multimodal agent execution | Claw-Eval: Towards Trustworthy Evaluation of Autonomous Agents |
| τ³-bench results τ³-Bench Tool-Agent-User Evaluation | 34.7 | Corrected customer-service tasks plus knowledge and voice evaluation modes | Long-horizon, multimodal, and knowledge-aware tool use | Official τ³-bench repository and release notes |