GEM-4C 558 -4%
GPT-61S 555 -3.8%
CL-SN55 553 -3.8%
CL-OP55 551 -4%
GPT-61TERR 547 -3.5%
GEM-4CM 546 -4%
CL-OP55EH 542 -3.9%
GPT-61SOLXH 541 -3.6%
GPT-61SOLH 527 -3.7%
CL-SN55XH 523 -3.5%
GEM-4CL 520 -3.9%
GPT-61TERRXH 507 -3.4%
CL-OP55H 506 -4%
GPT-61SOLM 500 -3.5%
KIMI-K31P 499 -3.5%
GEM-4AR 498 -3.9%
GPT-61TERRH 490 -3.2%
CL-OP55M 483 -3.6%
MUSE-WONDFUL 479 -3.8%
CL-SN55H 467 -3.3%
CL-HK55 463 -2.9%
GPT-61TERRM 462 -3.1%
GPT-6A 451 -4.7%
MUSE-WOND 451 -3.6%
CL-HK55XH 450 -3%
GPT-6ASTRXH 440 -4.8%
GROK-47 440 -3.1%
GROK-47H 438 -2.9%
GPT-6ASTRH 437 -4.8%
DSK-V-340615 436 -2.9%
QWEN-38X 428 -2.9%
GPT-61SOLL 423 -3.2%
GLM-53 420 -3%
MIMO-V26P 420 -2.6%
CL-HK55H 419 -2.8%
LING-31F1019 417 -2.6%
GEM-38F 416 -2.8%
GPT-6ASTRM 415 -4.6%
MMAX-M31F 414 -2.8%
GEM-38FM 412 -2.8%
GPT-61TERRNO 411 -2.8%
GROK-47L 410 -2.8%
DSK-V-3E48BD 410 -2.6%
GPT-6L 407 -2.6%
GLM-53F 407 -2.6%
STEP-5 405 -2.6%
CL-FABL51XH 403 -4.7%
GPT-61TERRL 403 -2.9%
MIMO-V26F 401 -2.4%
CL-FABL51 399 -4.5%
CL-SN55M 395 -2.9%
CL-OP55L 393 -3.4%
MIST-LARG4 390 -2.7%
CL-FABL51H 386 -4.5%
CL-HK55M 386 -2.3%
GPT-6LUNAXH 377 -2.6%
MUSE-SPAROPE 375 -2.6%
MIST-LARG4M 371 -2.4%
DSK-V4P 365 -2.4%
GPT-6LUNAH 365 -2.1%
GLM-53L 364 -2.4%
CL-SN55L 362 -2.7%
GEM-38FL 361 -2.4%
GPT-6ASTRL 359 -4.5%
CL-FABL51M 359 -4.5%
QWEN-827B 349 -2.2%
CL-HK55L 347 -2%
MMAX-M3 345 -2%
GPT-6LUNAM 344 -2.3%
DSK-V-02811D 344 -2%
K2-HORI375BA 342 -2%
REFL-BEAM 338 -2%
QWEN-827BM 332 -1.8%
CL-FABL51L 329 -4.4%
QWEN-827BL 320 -1.8%
NEMO-35SUPEV 313 -1.9%
INKL 295 -1.7%
NVID-NEMO3UL 295 -1.3%
GEM-35FLITE 295 -1.3%
GPT-6LUNAL 287 -1.4%
GPT-6LUNANON 286 -1%
NANB-333B 285 -2.7%
QWEN-827BNON 282 -1.4%
COMM-APLUS 266 -1.1%
MUSE-GLIM 263 -1.1%
NEMO-35LIGH 261 -0.8%
GRAN-428B 258 -0.8%
MIST-SMAL4 252 -0.4%
INKL-SM 235 -2.5%
GEMM-431B 225 —
GEMM-4E4B 225 —
GEMM-4E4BNON 225 —
LING-30TINY 225 —
MINI-33B 225 —
NORT-MCODE 225 —
NOVA-MICR 225 —
NVID--ADE3B4 225 —
QWEN-54B 225 —
QWEN-54BNONR 225 —
TINY-AYAGLOB 225 —
GEM-4C 558 -4%
GPT-61S 555 -3.8%
CL-SN55 553 -3.8%
CL-OP55 551 -4%
GPT-61TERR 547 -3.5%
GEM-4CM 546 -4%
CL-OP55EH 542 -3.9%
GPT-61SOLXH 541 -3.6%
GPT-61SOLH 527 -3.7%
CL-SN55XH 523 -3.5%
GEM-4CL 520 -3.9%
GPT-61TERRXH 507 -3.4%
CL-OP55H 506 -4%
GPT-61SOLM 500 -3.5%
KIMI-K31P 499 -3.5%
GEM-4AR 498 -3.9%
GPT-61TERRH 490 -3.2%
CL-OP55M 483 -3.6%
MUSE-WONDFUL 479 -3.8%
CL-SN55H 467 -3.3%
CL-HK55 463 -2.9%
GPT-61TERRM 462 -3.1%
GPT-6A 451 -4.7%
MUSE-WOND 451 -3.6%
CL-HK55XH 450 -3%
GPT-6ASTRXH 440 -4.8%
GROK-47 440 -3.1%
GROK-47H 438 -2.9%
GPT-6ASTRH 437 -4.8%
DSK-V-340615 436 -2.9%
QWEN-38X 428 -2.9%
GPT-61SOLL 423 -3.2%
GLM-53 420 -3%
MIMO-V26P 420 -2.6%
CL-HK55H 419 -2.8%
LING-31F1019 417 -2.6%
GEM-38F 416 -2.8%
GPT-6ASTRM 415 -4.6%
MMAX-M31F 414 -2.8%
GEM-38FM 412 -2.8%
GPT-61TERRNO 411 -2.8%
GROK-47L 410 -2.8%
DSK-V-3E48BD 410 -2.6%
GPT-6L 407 -2.6%
GLM-53F 407 -2.6%
STEP-5 405 -2.6%
CL-FABL51XH 403 -4.7%
GPT-61TERRL 403 -2.9%
MIMO-V26F 401 -2.4%
CL-FABL51 399 -4.5%
CL-SN55M 395 -2.9%
CL-OP55L 393 -3.4%
MIST-LARG4 390 -2.7%
CL-FABL51H 386 -4.5%
CL-HK55M 386 -2.3%
GPT-6LUNAXH 377 -2.6%
MUSE-SPAROPE 375 -2.6%
MIST-LARG4M 371 -2.4%
DSK-V4P 365 -2.4%
GPT-6LUNAH 365 -2.1%
GLM-53L 364 -2.4%
CL-SN55L 362 -2.7%
GEM-38FL 361 -2.4%
GPT-6ASTRL 359 -4.5%
CL-FABL51M 359 -4.5%
QWEN-827B 349 -2.2%
CL-HK55L 347 -2%
MMAX-M3 345 -2%
GPT-6LUNAM 344 -2.3%
DSK-V-02811D 344 -2%
K2-HORI375BA 342 -2%
REFL-BEAM 338 -2%
QWEN-827BM 332 -1.8%
CL-FABL51L 329 -4.4%
QWEN-827BL 320 -1.8%
NEMO-35SUPEV 313 -1.9%
INKL 295 -1.7%
NVID-NEMO3UL 295 -1.3%
GEM-35FLITE 295 -1.3%
GPT-6LUNAL 287 -1.4%
GPT-6LUNANON 286 -1%
NANB-333B 285 -2.7%
QWEN-827BNON 282 -1.4%
COMM-APLUS 266 -1.1%
MUSE-GLIM 263 -1.1%
NEMO-35LIGH 261 -0.8%
GRAN-428B 258 -0.8%
MIST-SMAL4 252 -0.4%
INKL-SM 235 -2.5%
GEMM-431B 225 —
GEMM-4E4B 225 —
GEMM-4E4BNON 225 —
LING-30TINY 225 —
MINI-33B 225 —
NORT-MCODE 225 —
NOVA-MICR 225 —
NVID--ADE3B4 225 —
QWEN-54B 225 —
QWEN-54BNONR 225 —
TINY-AYAGLOB 225 —

Delayed ticker data. Last calculated 5 Nov 2026, 11:04 pm Sydney time.

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Cohere Releases Command A+: Open Weights, 86% Non-Hallucination, First Model in 14 Months

Cohere has released Command A+, an open-weights language model scoring 37 on the Artificial Analysis Intelligence Index. The release puts Command A+ in the same tier as Claude 4.5 Haiku, NVIDIA Nemotron 3 Super, and Gemini 3.1 Flash-Lite — capable mid-range models positioned for enterprise deployment rather than frontier reasoning.

It is the company’s first significant model release in over 14 months, following the original Command A.

Where It Leads

Command A+‘s clearest differentiation is on knowledge reliability. It scores 86% on AA-Omniscience Non-Hallucination, approximately three percentage points ahead of the next-best model at this intelligence tier. The trade-off is intentional: its AA-Omniscience Accuracy is 9%, which means the model tends to decline when uncertain rather than confabulate. For legal, financial, and compliance deployments — Cohere’s core enterprise markets — that calibration is the right one.

On output speed, Command A+ produces approximately 281 tokens per second on Cohere’s API, faster than Claude 4.5 Haiku, GPT-5.4 nano, and Grok 4.3, while still trailing Gemini 3.1 Flash-Lite Preview at 304 tokens per second.

Where It Trails

Command A+ has measurable gaps on the hardest benchmarks:

BenchmarkCommand A+Tier Context
HLE~11%Below Haiku class average
GPQA Diamond76%Below Haiku class average
Terminal-Bench Hard~25%Well below coding-optimised models
SciCode~38%Below scientific model class
MMMU-Pro (vision)63%Between Haiku (59%) and GPT-5.4 nano (65%)

The Terminal-Bench Hard gap is the most commercially relevant. At 25%, Command A+ is not a coding agent deployment option, even for tasks within its intelligence tier. That limit places it squarely in content, retrieval, and document-processing workflows rather than developer tools.

The Open Weights Angle

Command A+ is released with open weights — a significant shift for Cohere, which has historically positioned its models as API-only, enterprise-licensed products. Opening the weights is a competitive response to an environment where Qwen3.6-35B, Mistral Medium 3.5, and Llama 4 Scout have raised the baseline expectations for what open-weights models should offer.

For Cohere’s enterprise customers, the open-weights release enables self-hosted deployment, which has become a hard requirement for regulated-industry customers in financial services and healthcare across Europe — markets that Cohere targeted directly through its acquisition of Aleph Alpha earlier this year.

Context: Cohere After Aleph Alpha

Cohere’s $20 billion acquisition of Aleph Alpha gave it direct access to European government and financial services contracts, a dedicated on-premises deployment stack, and 30 researchers specialised in domain-specific AI. Command A+ is the first new Cohere model to enter those markets as a self-hosted option. Whether the combined entity can hold that enterprise position against DeepSeek V4 — which offers a substantially higher intelligence index score under an MIT licence — will determine how durable this release is commercially.